{
 "about": "My latest comments on published entries, exactly as last saved, each with the history of the entry: its classification before my review, the outcome of the independent evaluation, and, where the evaluation did not adopt my suggestion in full, the models’ position. I deferred to the models’ judgment in these disagreements.",
 "comments": [
  {
   "id": "P001-C03-E001",
   "work_id": "P001",
   "url": "https://pmaronow.github.io/llm-audit/works/P001/#P001-C03-E001",
   "comment": "Agreed.",
   "suggestion": null,
   "classification_before_review": {
    "class": "error",
    "severity": "Moderate"
   },
   "classification_after_evaluation": {
    "class": "error",
    "severity": "Moderate"
   },
   "classification_now": {
    "class": "error",
    "severity": "Moderate"
   },
   "outcome": "no_change",
   "outcome_text": "No change was needed: my comment agrees with the entry.",
   "disagreement": null
  },
  {
   "id": "P001-C04-E001",
   "work_id": "P001",
   "url": "https://pmaronow.github.io/llm-audit/works/P001/#P001-C04-E001",
   "comment": "Agreed.",
   "suggestion": null,
   "classification_before_review": {
    "class": "error",
    "severity": "Moderate"
   },
   "classification_after_evaluation": {
    "class": "error",
    "severity": "Moderate"
   },
   "classification_now": {
    "class": "error",
    "severity": "Moderate"
   },
   "outcome": "no_change",
   "outcome_text": "No change was needed: my comment agrees with the entry.",
   "disagreement": null
  },
  {
   "id": "P001-C05-N005",
   "work_id": "P001",
   "url": "https://pmaronow.github.io/llm-audit/works/P001/#P001-C05-N005",
   "comment": "This is sufficiently ambiguous that I think it is worth classifying as a moderate error for reader attention.",
   "suggestion": "Moderate",
   "classification_before_review": {
    "class": "error",
    "severity": "Moderate"
   },
   "classification_after_evaluation": {
    "class": "error",
    "severity": "Moderate"
   },
   "classification_now": {
    "class": "error",
    "severity": "Moderate"
   },
   "outcome": "no_change",
   "outcome_text": "The Moderate classification is retained.",
   "disagreement": null
  },
  {
   "id": "P001-C06-E007",
   "work_id": "P001",
   "url": "https://pmaronow.github.io/llm-audit/works/P001/#P001-C06-E007",
   "comment": "Agreed.",
   "suggestion": null,
   "classification_before_review": {
    "class": "error",
    "severity": "Moderate"
   },
   "classification_after_evaluation": {
    "class": "error",
    "severity": "Moderate"
   },
   "classification_now": {
    "class": "error",
    "severity": "Moderate"
   },
   "outcome": "no_change",
   "outcome_text": "No change was needed: my comment agrees with the entry.",
   "disagreement": null
  },
  {
   "id": "P001-C07-A001",
   "work_id": "P001",
   "url": "https://pmaronow.github.io/llm-audit/works/P001/#P001-C07-A001",
   "comment": "I think this is worse than represented. This expression -- even once corrected is only _equivalent_ to the AMCE under ignorability. The definition should not condition on D,X; rather it should be a separate theorem.",
   "suggestion": "Moderate",
   "classification_before_review": {
    "class": "clarity_note",
    "severity": null
   },
   "classification_after_evaluation": {
    "class": "clarity_note",
    "severity": null
   },
   "classification_now": {
    "class": "clarity_note",
    "severity": null
   },
   "outcome": "not_adopted",
   "outcome_text": "Not adopted: the entry stays a clarity note, not the Moderate error I suggested.",
   "disagreement": {
    "models_position": "With the conditioning values held fixed, the corrected expression equals the intended average causal derivative without ignorability; its equality with the observational quantity is the separate claim that needs ignorability. The correct reading is clear from context, so under the classification rule this is a clarity note."
   }
  },
  {
   "id": "P001-C07-E007",
   "work_id": "P001",
   "url": "https://pmaronow.github.io/llm-audit/works/P001/#P001-C07-E007",
   "comment": "Yes, this should be clarified.",
   "suggestion": null,
   "classification_before_review": {
    "class": "error",
    "severity": "Moderate"
   },
   "classification_after_evaluation": {
    "class": "error",
    "severity": "Moderate"
   },
   "classification_now": {
    "class": "error",
    "severity": "Moderate"
   },
   "outcome": "no_change",
   "outcome_text": "No change was needed: the entry already gives that clarification.",
   "disagreement": null
  },
  {
   "id": "P001-C07-E010",
   "work_id": "P001",
   "url": "https://pmaronow.github.io/llm-audit/works/P001/#P001-C07-E010",
   "comment": "Agreed.",
   "suggestion": null,
   "classification_before_review": {
    "class": "error",
    "severity": "Moderate"
   },
   "classification_after_evaluation": {
    "class": "error",
    "severity": "Moderate"
   },
   "classification_now": {
    "class": "error",
    "severity": "Moderate"
   },
   "outcome": "no_change",
   "outcome_text": "No change was needed: my comment agrees with the entry.",
   "disagreement": null
  },
  {
   "id": "P001-C07-E011",
   "work_id": "P001",
   "url": "https://pmaronow.github.io/llm-audit/works/P001/#P001-C07-E011",
   "comment": "Agreed.",
   "suggestion": null,
   "classification_before_review": {
    "class": "error",
    "severity": "Moderate"
   },
   "classification_after_evaluation": {
    "class": "error",
    "severity": "Moderate"
   },
   "classification_now": {
    "class": "error",
    "severity": "Moderate"
   },
   "outcome": "no_change",
   "outcome_text": "No change was needed: my comment agrees with the entry.",
   "disagreement": null
  },
  {
   "id": "P006-E001",
   "work_id": "P006",
   "url": "https://pmaronow.github.io/llm-audit/works/P006/#P006-E001",
   "comment": "I agree.",
   "suggestion": null,
   "classification_before_review": {
    "class": "error",
    "severity": "Major"
   },
   "classification_after_evaluation": {
    "class": "error",
    "severity": "Major"
   },
   "classification_now": {
    "class": "error",
    "severity": "Major"
   },
   "outcome": "no_change",
   "outcome_text": "My comment agrees with the entry; no change.",
   "disagreement": null
  },
  {
   "id": "P008-N012",
   "work_id": "P008",
   "url": "https://pmaronow.github.io/llm-audit/works/P008/#P008-N012",
   "comment": "I agree. This needs further investigation -- the correct reference distribution should be used here.",
   "suggestion": null,
   "classification_before_review": {
    "class": "error",
    "severity": "Major"
   },
   "classification_after_evaluation": {
    "class": "error",
    "severity": "Major"
   },
   "classification_now": {
    "class": "error",
    "severity": "Major"
   },
   "outcome": "no_change",
   "outcome_text": "My comment supports further investigation; no change.",
   "disagreement": null
  },
  {
   "id": "P010-N011",
   "work_id": "P010",
   "url": "https://pmaronow.github.io/llm-audit/works/P010/#P010-N011",
   "comment": "I think this is a minor expositional error -- the plots make it clear.",
   "suggestion": "Minor",
   "classification_before_review": {
    "class": "error",
    "severity": "Moderate"
   },
   "classification_after_evaluation": {
    "class": "error",
    "severity": "Moderate"
   },
   "classification_now": {
    "class": "error",
    "severity": "Moderate"
   },
   "outcome": "not_adopted",
   "outcome_text": "Not adopted: the entry stays a Moderate error, not the Minor rating I suggested.",
   "disagreement": {
    "models_position": "The broad warning and the plotted values survive, but the claim in the text about the overlap of the intervals is false and hides two estimated preference contrasts. A reader who relies on the text takes away materially less than the estimates show: Moderate."
   }
  },
  {
   "id": "P011-E001",
   "work_id": "P011",
   "url": "https://pmaronow.github.io/llm-audit/works/P011/#P011-E001",
   "comment": "I investigated this, in part with help from an LLM. I agree, but want to suggest a more broad type of correction. The following is LLM-generated output, generated at my supervision:\n\n# An additional condition for conservative inference\n\nRetain Assumptions 1–2. Write $X_i=\\theta_iY_i$, and let $A_R$ be the known-edge adjacency matrix. Choose fixed integer caps\n$\\deg_{A^{\\mathcal O}}(i)\\le h_i\\le\\min\\{d_i,n-1\\}$, with $\\max_i h_i\\le D<\\infty$; the original maximization may retain larger supplied caps. Define\n\n\\[\nr_i=\\sum_j(A_R)_{ij},\\qquad\ns_i=h_i-r_i,\\qquad\nK_R=I+A_R+\\operatorname{diag}(s_i).\n\\]\n\nThus $s_i$ is the capacity left for unknown edges. Let $v$ be the variance inputs, and optionally add a fixed correction $Q\\succeq0$, with $\\|Q\\|_{\\mathrm{op}}=O(1)$:\n\n\\[\n\\widehat V\n=\\frac1{n^2}\\max_{A\\in\\mathcal A(Z)}v^\\top(I+A)v\n+\\frac1{n^2}v^\\top Qv.\n\\]\n\nFor the published estimator, $v=X-\\hat\\beta\\mathbf1$ and $Q=0$. Regression alternatives below change the variance inputs and retain the original point estimator.\n\n**Additional condition.** Write $v=X-E[X]+\\delta+a$, where $\\delta$ is deterministic, $\\|\\delta\\|^2=O(n)$, $\\|a\\|^2/n\\to_p0$, and $\\max_i v_i^2/n\\to_p0$. Require\n\n\\[\n\\boxed{\\displaystyle\n\\liminf_{n\\to\\infty}\\frac{\\delta^\\top(K_R+Q)\\delta}{n}\\ge0.}\n\\tag{C}\n\\]\n\nFor published centering, $\\delta=E[X]-\\beta\\mathbf1$, and the other requirements follow from Assumption 2. A sufficient spectral certificate, when $\\delta\\in\\operatorname{col}(U)$ and $U^\\top U=I$, is\n\n\\[\n\\lambda_{\\min}\\{U^\\top(K_R+Q)U\\}\\ge-\\varepsilon_n,\n\\qquad \\varepsilon_n\\to0.\n\\]\n\nCondition (C) gives asymptotic variance conservativeness and at-least-nominal Wald coverage. It restricts the relevant mean directions; it does not require the full matrix's negative eigenvalues to vanish.\n\nThe following are sufficient routes. The first two use published centering, whose mean deviations are uniformly bounded.\n\n- **Few known edges.** The number of known edges is $o(n)$. This includes degree-only constraints, with no known edges.\n\n- **The known graph is nearly a union of disjoint cliques.** Deleting $o(n)$ known edges leaves vertex-disjoint clique components. A simple sufficient case is that only $o(n)$ known edges belong to components that are not cliques. The remaining clique edges may number $O(n)$, and their degree caps need not be saturated. Exact disjoint cliques are a special case; overlapping cliques alone do not suffice.\n\n- **Enough remaining degree capacity.** For every vertex, $h_i\\ge2r_i-1$. Then\n  \\[\n  K_R=A_R+\\operatorname{diag}(r_i)\n      +\\operatorname{diag}(1+h_i-2r_i)\\succeq0.\n  \\]\n\n- **A compatible mean space.** The possible mean deviations lie in a specified space $\\operatorname{col}(U)$, and $U^\\top K_RU$ is positive semidefinite, or its minimum eigenvalue tends to zero from below. Common transformed means, $E[X_i]=\\beta$, give $\\delta=0$.\n\n- **An explicit correction is added.** One universal choice is $Q=(-K_R)_+$, the negative spectral part of $K_R$ with its signs reversed. Alternatively, choose a known-edge set $F$ whose deletion leaves vertex-disjoint cliques, and add\n  \\[\n  \\frac1{n^2}\\sum_{\\{i,j\\}\\in F}(v_i-v_j)^2.\n  \\]\n  This uses $Q=L_F$, the Laplacian of the deleted edges; $F$ need not be $o(n)$. Both corrections are added after maximization and preserve the original maximizing graph.\n\n- **Residuals from a well-specified regression.** Suppose $E[Y_i]=f_i^\\top\\gamma$, with nonrandom regressors. Fit by $\\theta_i^2$-weighted least squares and use $v_i=\\theta_i(Y_i-f_i^\\top\\hat\\gamma)$. It is enough that the transformed design $W$, with rows $\\theta_i f_i^\\top$, has rank $o(n)$; a fixed number of regressors suffices. The fitted weighted means have average squared prediction error tending to zero, so (C) holds with $\\delta=0$.\n\n- **Residuals from a nearly well-specified regression.** Use the same fit, require $\\operatorname{rank}(W)=o(n)$, and assume\n  \\[\n  \\inf_\\gamma\\frac1n\\sum_i\\theta_i^2\n      \\{E[Y_i]-f_i^\\top\\gamma\\}^2\\longrightarrow0.\n  \\]\n  The remaining weighted mean error has $\\|\\delta\\|^2/n\\to0$, which implies (C). More generally, either residual case works whenever\n  \\[\n  \\frac1n\\sum_i\\theta_i^2\n      \\{\\hat m_i-E[Y_i]\\}^2\\to_p0.\n  \\]\n\nThe regression cases also give consistency of the true-graph residual oracle. The maximized bound is conservative and need not be consistent. Correct specification alone is insufficient for arbitrary unweighted fitting under the paper's assumptions.\n\n**Why this works.** Proper-color the known graph, then form sorted cliques separately within each color and remaining-capacity class. This gives a compatible graph and establishes\n\\[\nn\\widehat V\\ge\\frac1n v^\\top(K_R+Q)v-o_p(1).\n\\]\nMoreover, $K_R-(I+A^{\\mathcal O})$ is the Laplacian of the unknown true edges plus a nonnegative diagonal matrix. Its covariance contribution is therefore nonnegative; (C) controls the remaining mean contribution. The covariance/mean separation is inspired by [Yap's Theorem 2](https://arxiv.org/pdf/2603.11497).\n\nUpdated after LLMs rendered their decision.",
   "suggestion": null,
   "classification_before_review": {
    "class": "error",
    "severity": "Critical"
   },
   "classification_after_evaluation": {
    "class": "error",
    "severity": "Critical"
   },
   "classification_now": {
    "class": "error",
    "severity": "Critical"
   },
   "outcome": "incorporated",
   "outcome_text": "Incorporated. The correction was broadened to include the additional condition in my comment and the other sufficient routes to conservative inference that the independent check established. The entry remains Critical: these conditions do not restore the paper’s original unrestricted guarantees.",
   "disagreement": null
  },
  {
   "id": "P012-E001",
   "work_id": "P012",
   "url": "https://pmaronow.github.io/llm-audit/works/P012/#P012-E001",
   "comment": "Agreed.",
   "suggestion": null,
   "classification_before_review": {
    "class": "error",
    "severity": "Moderate"
   },
   "classification_after_evaluation": {
    "class": "error",
    "severity": "Moderate"
   },
   "classification_now": {
    "class": "error",
    "severity": "Moderate"
   },
   "outcome": "no_change",
   "outcome_text": "No change was needed: my comment agrees with the entry.",
   "disagreement": null
  },
  {
   "id": "P012-N007",
   "work_id": "P012",
   "url": "https://pmaronow.github.io/llm-audit/works/P012/#P012-N007",
   "comment": "I think this is actually a major correction, because it obviates much of the motivation of the paper. People should know there is a closed-form solution.",
   "suggestion": "Major",
   "classification_before_review": {
    "class": "error",
    "severity": "Moderate"
   },
   "classification_after_evaluation": {
    "class": "error",
    "severity": "Moderate"
   },
   "classification_now": {
    "class": "error",
    "severity": "Moderate"
   },
   "outcome": "incorporated_in_part",
   "outcome_text": "Incorporated in part. The entry now says that, for these four endpoints, the closed-form formulas replace the search in the appendix and the convergence argument, and shorten the proofs of Results 2 and 3. My suggested rating (Major) was not adopted.",
   "disagreement": {
    "models_position": "The same observed data still leave homophily and preferential recruitment unidentified over nontrivial intervals, and the identification results and the verified example survive. Readers need the direct formulas, but the consequence for the paper’s contribution is limited: Moderate."
   }
  },
  {
   "id": "P013-E001",
   "work_id": "P013",
   "url": "https://pmaronow.github.io/llm-audit/works/P013/#P013-E001",
   "comment": "Agreed.",
   "suggestion": null,
   "classification_before_review": {
    "class": "error",
    "severity": "Moderate"
   },
   "classification_after_evaluation": {
    "class": "error",
    "severity": "Moderate"
   },
   "classification_now": {
    "class": "error",
    "severity": "Moderate"
   },
   "outcome": "no_change",
   "outcome_text": "No change was needed: my comment agrees with the entry.",
   "disagreement": null
  },
  {
   "id": "P013-E003",
   "work_id": "P013",
   "url": "https://pmaronow.github.io/llm-audit/works/P013/#P013-E003",
   "comment": "Agreed.",
   "suggestion": null,
   "classification_before_review": {
    "class": "error",
    "severity": "Moderate"
   },
   "classification_after_evaluation": {
    "class": "error",
    "severity": "Moderate"
   },
   "classification_now": {
    "class": "error",
    "severity": "Moderate"
   },
   "outcome": "no_change",
   "outcome_text": "No change was needed: my comment agrees with the entry.",
   "disagreement": null
  },
  {
   "id": "P013-N012",
   "work_id": "P013",
   "url": "https://pmaronow.github.io/llm-audit/works/P013/#P013-N012",
   "comment": "This was not a problem with this paper, but rather the later software package.",
   "suggestion": "not_an_error",
   "classification_before_review": {
    "class": "error",
    "severity": "Moderate"
   },
   "classification_after_evaluation": {
    "class": "error",
    "severity": "Moderate"
   },
   "classification_now": {
    "class": "error",
    "severity": "Moderate"
   },
   "outcome": "incorporated_in_part",
   "outcome_text": "Incorporated in part. The entry now attributes the defect to the later package, gives the package’s full list of authors without assigning responsibility for the defect, and says that its use for the paper’s original calculations is not established. My suggestion that this is not an error was not adopted.",
   "disagreement": {
    "models_position": "The scope of the audit includes a package that implements the paper’s method, and the defect in its assignment probabilities is verified. No published table is shown to be wrong: Moderate."
   }
  },
  {
   "id": "P013-N020",
   "work_id": "P013",
   "url": "https://pmaronow.github.io/llm-audit/works/P013/#P013-N020",
   "comment": "Agreed.",
   "suggestion": null,
   "classification_before_review": {
    "class": "error",
    "severity": "Moderate"
   },
   "classification_after_evaluation": {
    "class": "error",
    "severity": "Moderate"
   },
   "classification_now": {
    "class": "error",
    "severity": "Moderate"
   },
   "outcome": "no_change",
   "outcome_text": "No change was needed: my comment agrees with the entry.",
   "disagreement": null
  },
  {
   "id": "P014-E001",
   "work_id": "P014",
   "url": "https://pmaronow.github.io/llm-audit/works/P014/#P014-E001",
   "comment": "Agreed.",
   "suggestion": null,
   "classification_before_review": {
    "class": "error",
    "severity": "Moderate"
   },
   "classification_after_evaluation": {
    "class": "error",
    "severity": "Moderate"
   },
   "classification_now": {
    "class": "error",
    "severity": "Moderate"
   },
   "outcome": "no_change",
   "outcome_text": "No change was needed: my comment agrees with the entry.",
   "disagreement": null
  },
  {
   "id": "P015-E006",
   "work_id": "P015",
   "url": "https://pmaronow.github.io/llm-audit/works/P015/#P015-E006",
   "comment": "I think this is too literal a reading of the manuscript. I think it's clear from context what is meant.",
   "suggestion": "Minor",
   "classification_before_review": {
    "class": "error",
    "severity": "Moderate"
   },
   "classification_after_evaluation": {
    "class": "error",
    "severity": "Moderate"
   },
   "classification_now": {
    "class": "error",
    "severity": "Moderate"
   },
   "outcome": "not_adopted",
   "outcome_text": "Not adopted: the entry stays a Moderate error, not the Minor rating I suggested.",
   "disagreement": {
    "models_position": "Under its natural reading, the passage also makes claims about the support of the endpoints and about what an estimator cannot do. These fail even though the regression theorem holds, so a reader who applies the stated diagnostic can be materially misled: Moderate."
   }
  },
  {
   "id": "P015-E008",
   "work_id": "P015",
   "url": "https://pmaronow.github.io/llm-audit/works/P015/#P015-E008",
   "comment": "Agreed.",
   "suggestion": "Moderate",
   "classification_before_review": {
    "class": "error",
    "severity": "Moderate"
   },
   "classification_after_evaluation": {
    "class": "error",
    "severity": "Moderate"
   },
   "classification_now": {
    "class": "error",
    "severity": "Moderate"
   },
   "outcome": "no_change",
   "outcome_text": "No change was needed: my comment agrees with the entry and its Moderate rating.",
   "disagreement": null
  },
  {
   "id": "P017-E001",
   "work_id": "P017",
   "url": "https://pmaronow.github.io/llm-audit/works/P017/#P017-E001",
   "comment": "Agreed. I think this is a fairly minor regularity condition, but it's worth flagging.",
   "suggestion": null,
   "classification_before_review": {
    "class": "error",
    "severity": "Moderate"
   },
   "classification_after_evaluation": {
    "class": "error",
    "severity": "Moderate"
   },
   "classification_now": {
    "class": "error",
    "severity": "Moderate"
   },
   "outcome": "no_change",
   "outcome_text": "No change was needed. The entry remains a Moderate error: the printed target can fail to be a weighted causal effect, although the numerical results stand and the repair is routine.",
   "disagreement": null
  },
  {
   "id": "P018-E003",
   "work_id": "P018",
   "url": "https://pmaronow.github.io/llm-audit/works/P018/#P018-E003",
   "comment": "Updated after the LLMs rendered a decision. I think this is mostly a regularity condition in context, but I'm fine classifying it as moderate.",
   "suggestion": null,
   "classification_before_review": {
    "class": "error",
    "severity": "Minor"
   },
   "classification_after_evaluation": {
    "class": "error",
    "severity": "Moderate"
   },
   "classification_now": {
    "class": "error",
    "severity": "Moderate"
   },
   "outcome": "no_change",
   "outcome_text": "My comment accepts the current Moderate classification; no change.",
   "disagreement": null
  },
  {
   "id": "P023-E001",
   "work_id": "P023",
   "url": "https://pmaronow.github.io/llm-audit/works/P023/#P023-E001",
   "comment": "I disagree. I think this is a wording issue. Below is a response from a conversation with an LLM.\n\nThe literal claim that finite-degree polynomial OLS uses solely close-election outcomes is false. That fact is a property of the design weights, not a rare outcome realization. With residualized treatment r, the coefficient is r'Y/(r'r); a remote outcome with r_i != 0 enters for every realized outcome vector. The exact House-data certificates were independently rerun during this review and passed for both specifications and both sample conventions.\n\nThe handpicked outcome vectors in the original illustration can themselves be rare. They demonstrate data dependence and do not measure the frequency of a substantively large OLS–RD discrepancy. Under the exactly specified model and shared-consistency assumptions used in E001, for every fixed positive tolerance epsilon, P(|OLS - RD| > epsilon) tends to zero. Nonzero remote weights do not contradict that conclusion.\n\nOn the full-paper reading, the explicit separation of exact coarsened equivalence from approximate polynomial equivalence, and Appendix B's common-cutoff-target argument, support treating the “solely” sentences as loose wording. E001 does not establish a failure of those equivalence results, a changed application estimate, or different causal bias or external validity. In particular, the finished block's claim that the unqualified transfer of concerns “falls with it” exceeds what its weight calculation proves: concerns arising from the common cutoff target and its identifying assumptions can still be shared.\n\n**Recommendation:** reclassify E001 as a clarity note with a targeted wording correction. Major is not justified by E001 alone. This reassessment is saved separately; canonical ratings, validation records, and generated publication files have not been changed by this chat.\n\nSuggested wording: “The binned-control OLS estimate equals the close-election RD estimate. An exactly specified polynomial regression estimates the same cutoff jump asymptotically while also using distant outcomes. Concerns arising from that cutoff target and its causal interpretation can apply to both approaches.”\n\nThis recommendation is limited to E001. The separate approximation-condition issue in E002 is unaffected.\n",
   "suggestion": "clarity_note",
   "classification_before_review": {
    "class": "error",
    "severity": "Major"
   },
   "classification_after_evaluation": {
    "class": "error",
    "severity": "Moderate"
   },
   "classification_now": {
    "class": "error",
    "severity": "Moderate"
   },
   "outcome": "incorporated_in_part",
   "outcome_text": "Incorporated in part. The rating was lowered from Major to Moderate, the claim that the concerns shared with close-election studies fall with this point was withdrawn, and the entry was redrafted. My suggested classification (clarity note) was not adopted.",
   "disagreement": {
    "models_position": "Under its natural reading, the claim that polynomial regression uses only close-election outcomes is false and can mislead readers about which data the estimate depends on, although the equivalence results, the reported estimates and the shared causal concerns survive: Moderate."
   }
  },
  {
   "id": "P026-E001",
   "work_id": "P026",
   "url": "https://pmaronow.github.io/llm-audit/works/P026/#P026-E001",
   "comment": "Yes, agreed. This was informal about pairwise vs joint independence.",
   "suggestion": null,
   "classification_before_review": {
    "class": "error",
    "severity": "Moderate"
   },
   "classification_after_evaluation": {
    "class": "error",
    "severity": "Moderate"
   },
   "classification_now": {
    "class": "error",
    "severity": "Moderate"
   },
   "outcome": "no_change",
   "outcome_text": "No change was needed. The independent check upheld the six-unit counterexample, and the entry remains a Moderate error.",
   "disagreement": null
  },
  {
   "id": "P027-N002",
   "work_id": "P027",
   "url": "https://pmaronow.github.io/llm-audit/works/P027/#P027-N002",
   "comment": "\"tend to\" is deliberately heuristic. I believe this to be minor.",
   "suggestion": "Minor",
   "classification_before_review": {
    "class": "error",
    "severity": "Moderate"
   },
   "classification_after_evaluation": {
    "class": "clarity_note",
    "severity": null
   },
   "classification_now": {
    "class": "clarity_note",
    "severity": null
   },
   "outcome": "incorporated_in_part",
   "outcome_text": "Incorporated in part. The heuristic reading was accepted, and the entry was reclassified from a Moderate error to a clarity note, which goes further than the Minor rating I suggested.",
   "disagreement": {
    "models_position": "The heuristic reading was accepted; under the classification rule the passage is then a clarity note rather than a Minor error."
   }
  },
  {
   "id": "P027-N005",
   "work_id": "P027",
   "url": "https://pmaronow.github.io/llm-audit/works/P027/#P027-N005",
   "comment": "This is a clarity point, not an error.",
   "suggestion": "Minor",
   "classification_before_review": {
    "class": "error",
    "severity": "Moderate"
   },
   "classification_after_evaluation": {
    "class": "error",
    "severity": "Moderate"
   },
   "classification_now": {
    "class": "error",
    "severity": "Moderate"
   },
   "outcome": "not_adopted",
   "outcome_text": "Not adopted: the entry stays a Moderate error, not a clarity note or the Minor rating I suggested.",
   "disagreement": {
    "models_position": "The supplement does not explain its departure from the preprocessing that the main text recommends, so the reported simulations cannot evaluate the recommended procedure; in highlighted rows they differ from its possible bias by one to two orders of magnitude: Moderate."
   }
  },
  {
   "id": "P032-E001",
   "work_id": "P032",
   "url": "https://pmaronow.github.io/llm-audit/works/P032/#P032-E001",
   "comment": "Agreed. The sign condition was implicit to our understanding of the natural of these problems typically, though we did not state it explicitly.",
   "suggestion": null,
   "classification_before_review": {
    "class": "error",
    "severity": "Moderate"
   },
   "classification_after_evaluation": {
    "class": "error",
    "severity": "Moderate"
   },
   "classification_now": {
    "class": "error",
    "severity": "Moderate"
   },
   "outcome": "no_change",
   "outcome_text": "No change was needed. The independent check found that the usual setting of these problems does not by itself ensure the sign condition, so the entry remains a Moderate error. My report that the condition was implicit is my own account and could not be verified independently.",
   "disagreement": null
  },
  {
   "id": "P033-N006",
   "work_id": "P033",
   "url": "https://pmaronow.github.io/llm-audit/works/P033/#P033-N006",
   "comment": "I disagree that this is an error. This is an implication of the motivation of the study. The implication is that the RD design is motivated by the semiparametric continuity assumption, not by stronger (e.g., linearity) assumptions. Thus, under those implicit (smoothness-only) conditions, the inference procedures used were inappropriately optimistic.\n\nUpdated after the LLM decision: I continue to believe this is a clarity note, not a major error. I think this is an example of the (current) LLMs being overly literal in interpretation.",
   "suggestion": "clarity_note",
   "classification_before_review": {
    "class": "error",
    "severity": "Major"
   },
   "classification_after_evaluation": {
    "class": "error",
    "severity": "Major"
   },
   "classification_now": {
    "class": "error",
    "severity": "Major"
   },
   "outcome": "incorporated_in_part",
   "outcome_text": "Incorporated in part. The entry now acknowledges the paper’s standard of continuity and smoothness and its warning about neglected bias, and qualifies the coverage claims of its affine example as pointwise. My suggested classification (clarity note) was not adopted.",
   "disagreement": {
    "models_position": "Smoothness also permits valid undersmoothing, and comparing standard errors does not establish which procedures the original studies used, or that a majority of the 45 articles used inference inappropriate under that standard. The diagnosis is central to the paper: Major."
   }
  },
  {
   "id": "P033-N010",
   "work_id": "P033",
   "url": "https://pmaronow.github.io/llm-audit/works/P033/#P033-N010",
   "comment": "I think this is minor. It's accurate _as-coded_ even if not on the authors' original scale.",
   "suggestion": "Minor",
   "classification_before_review": {
    "class": "error",
    "severity": "Moderate"
   },
   "classification_after_evaluation": {
    "class": "error",
    "severity": "Moderate"
   },
   "classification_now": {
    "class": "error",
    "severity": "Moderate"
   },
   "outcome": "incorporated_in_part",
   "outcome_text": "Incorporated in part. The entry now says that the code accurately computes the standardized slope that it targets, and describes a mismatch of comparisons rather than an arithmetic error. My suggested rating (Minor) was not adopted.",
   "disagreement": {
    "models_position": "The slope is a different contrast from the individual Cohen’s d that the paper presents, and using it changes a power classification and summary comparisons: Moderate."
   }
  },
  {
   "id": "P033-N011",
   "work_id": "P033",
   "url": "https://pmaronow.github.io/llm-audit/works/P033/#P033-N011",
   "comment": "This is a problem with rdrobust that we should have noticed. It's not an error of ours per se, but it does affect one of the conclusions.",
   "suggestion": "Moderate",
   "classification_before_review": {
    "class": "error",
    "severity": "Moderate"
   },
   "classification_after_evaluation": {
    "class": "error",
    "severity": "Moderate"
   },
   "classification_now": {
    "class": "error",
    "severity": "Moderate"
   },
   "outcome": "incorporated",
   "outcome_text": "Incorporated. The entry now attributes the defect to the default settings of the rdrobust package, which the paper’s call inherited. It remains a Moderate error, as I suggested.",
   "disagreement": null
  },
  {
   "id": "P035-E001",
   "work_id": "P035",
   "url": "https://pmaronow.github.io/llm-audit/works/P035/#P035-E001",
   "comment": "I think you are being far too literal in your reading of this. This is the inferred mechanism for the intervention. We are explicit that what we were randomizing, and why this is our inference from it.\n\nUpdated after decision rendered by LLMs. I continue to think this is a silly decision by the LLMs, and a warning to people using the (current iteration of) LLMs to judge severity of errors.",
   "suggestion": "clarity_note",
   "classification_before_review": {
    "class": "error",
    "severity": "Major"
   },
   "classification_after_evaluation": {
    "class": "error",
    "severity": "Major"
   },
   "classification_now": {
    "class": "error",
    "severity": "Major"
   },
   "outcome": "incorporated_in_part",
   "outcome_text": "Incorporated in part. The Introduction’s stated purpose and proposed mechanism are no longer treated as errors, and the quotation now includes the paper’s disclaimer about the nationwide campaign and its sentence on reinforcement. My suggested classification (clarity note) was not adopted.",
   "disagreement": {
    "models_position": "The abstract still attributes an effect to the initiative, and p. 5 calls the estimate a campaign effect, although the experiment is consistent with no contribution from the campaign. Major, for that narrower unsupported causal conclusion."
   }
  },
  {
   "id": "P037-E001",
   "work_id": "P037",
   "url": "https://pmaronow.github.io/llm-audit/works/P037/#P037-E001",
   "comment": "I disagree that this was an error. If the baseline treatment effect \\(\\tau_0\\) is constant across schools, this problem does not exist. This was the motivation (and was what was meant by the linear model expressed), in conjunction with trying to preserve efficiency with such a small number of schools. More generally, even if we don't believe this strictly, we just need a no-moderation-by-seed-composition assumption.\n\nBelow is an explanation produced by another LLM:\n\n# P037-E001: the intended baseline-effect assumption\n\nThe identification problem described in P037-E001 does not arise if the baseline treatment effect is constant across schools, under the maintained linear composition-response model. This constancy was the motivation for the model: the single coefficient $\\tau_0$ was intended to express a common baseline treatment effect.\n\nLet $D_i$ indicate school treatment, $C_i$ denote realized seed-group composition, and $q_i$ denote seed-eligible-group composition. The intended causal model is\n\n$$\nY_i(1,c)-Y_i(0)=\\tau_0+\\beta c.\n$$\n\nHere $\\tau_0$ is common across schools, and $\\beta$ is the common linear effect of composition. Untreated outcome levels may differ across schools. With the study's randomized assignments and equal school-treatment probabilities, a fixed analysis sample, and the design expectation $\\mu_i=\\mathbb E[C_i\\mid D_i=1,\\text{school }i]$ represented by the included composition controls, the regression identifies $\\beta$ through its expected normal equations, provided residual composition variation is positive. This is an identification statement, not a claim of exact finite-sample unbiasedness.\n\nThe decomposition emphasized by the finding,\n\n$$\nD_iC_i=D_i\\mu_i+D_i(C_i-\\mu_i),\n$$\n\ndoes not itself establish confounding. Under the intended model, both components enter the causal response with coefficient $\\beta$, while the baseline treatment contribution is exactly $\\tau_0D_i$, already included in the regression. There is no additional composition-related baseline treatment contribution for $D_iC_i$ to absorb.\n\nThe finding's counterexample instead assigns school-specific baseline treatment effects $\\tau_{0i}=-q_i$. Its spurious composition coefficient arises from that systematic moderation. The example establishes the need for an identifying assumption, but does not contradict the intended constant-$\\tau_0$ model. Differences between estimates from alternative specifications likewise do not establish that this assumption fails.\n\n## A weaker sufficient assumption\n\nStrict constancy is stronger than necessary. Write\n\n$$\nY_i(1,c)-Y_i(0)=\\tau_0+\\eta_i+\\beta c.\n$$\n\nSchool-specific baseline deviations $\\eta_i$ are permissible provided that their contribution to treatment effects is orthogonal to the composition variation used by the regression. Precisely, first remove realized block means from the regressors. Let $R_i$ be the residual from projecting the demeaned $D_iC_i$ on the other demeaned regressors, using design-expected cross-products to define the projection coefficients. The relevant condition is\n\n$$\n\\mathbb E\\!\\left[\\sum_i R_iD_i\\eta_i\\right]=0.\n$$\n\nUnder the preceding assumptions, the coefficient $\\beta^*$ from the expected normal equations satisfies\n\n$$\n\\beta^*-\\beta=\n\\frac{\\mathbb E[\\sum_i R_iD_i\\eta_i]}\n{\\mathbb E[\\sum_i R_i^2]}.\n$$\n\nSubstantively, this is a **no-moderation-by-eligible-composition assumption for the baseline treatment effect**, understood in the precise orthogonality sense above. Baseline-effect heterogeneity must not track expected seed composition in a way that the regression attributes to $\\beta$. This restriction covers variation both within and between blocks; block and covariate main effects do not automatically remove treatment-effect moderation. It permits heterogeneous baseline effects and the modeled causal composition effect $\\beta c$.\n\nThe supplement's equation (2), repeated in equations (4) and (6), was intended to express this restricted causal response. E001 should therefore distinguish a concern about the plausibility or explicitness of that restriction from an identification failure under the intended model. The common-baseline model eliminates the specific problem, and the weaker orthogonality restriction is sufficient for the same purpose.\n",
   "suggestion": "Minor",
   "classification_before_review": {
    "class": "error",
    "severity": "Major"
   },
   "classification_after_evaluation": {
    "class": "not_confirmed",
    "severity": null
   },
   "classification_now": {
    "class": "not_confirmed",
    "severity": null
   },
   "outcome": "incorporated_in_part",
   "outcome_text": "Incorporated in part. The entry is no longer classified as an error: it is now not confirmed, with no severity, rather than the Minor rating I suggested, and it is presented as a clarification of the scope of the model.",
   "disagreement": {
    "models_position": "The entry clarifies the scope of the model; under the classification rule this is not a confirmed error, so it carries no severity."
   }
  },
  {
   "id": "P037-E005",
   "work_id": "P037",
   "url": "https://pmaronow.github.io/llm-audit/works/P037/#P037-E005",
   "comment": "Agreed.",
   "suggestion": null,
   "classification_before_review": {
    "class": "error",
    "severity": "Moderate"
   },
   "classification_after_evaluation": {
    "class": "error",
    "severity": "Moderate"
   },
   "classification_now": {
    "class": "error",
    "severity": "Moderate"
   },
   "outcome": "no_change",
   "outcome_text": "No change was needed: my comment agrees with the entry.",
   "disagreement": null
  },
  {
   "id": "P037-N001",
   "work_id": "P037",
   "url": "https://pmaronow.github.io/llm-audit/works/P037/#P037-N001",
   "comment": "We already corrected this, no? It's fine to restate it here though.",
   "suggestion": null,
   "classification_before_review": {
    "class": "error",
    "severity": "Moderate"
   },
   "classification_after_evaluation": {
    "class": "error",
    "severity": "Moderate"
   },
   "classification_now": {
    "class": "error",
    "severity": "Moderate"
   },
   "outcome": "no_change",
   "outcome_text": "No change was needed: the entry already records that the official 2018 correction replaced 30% with 25%.",
   "disagreement": null
  },
  {
   "id": "P037-N006",
   "work_id": "P037",
   "url": "https://pmaronow.github.io/llm-audit/works/P037/#P037-N006",
   "comment": "I don't know that this is an error per se, so much as it was a suboptimal decision. HC0 SEs remain common -- in particular as raw influence function-based estimators (see, e.g., the DoubleML implementation). I agree that the correction presents more accurate SEs.\n\nUpdated after decision rendered by LLM. I think it is good that this is being flagged, as the SEs are misleadingly small.",
   "suggestion": "Moderate",
   "classification_before_review": {
    "class": "error",
    "severity": "Major"
   },
   "classification_after_evaluation": {
    "class": "error",
    "severity": "Major"
   },
   "classification_now": {
    "class": "error",
    "severity": "Major"
   },
   "outcome": "incorporated_in_part",
   "outcome_text": "Incorporated in part. The independent check confirmed that HC0 and influence-function variances remain in contemporary use; the entry, which makes no claim to the contrary, was kept. My suggested rating (Moderate) was not adopted.",
   "disagreement": {
    "models_position": "In the independently reproduced null experiments, the HC0 inference for these 49-school fits over-rejects materially, and the proposed correction keeps the same HC0 standard errors, improving only their rounding. The central significance claim is unsupported by that procedure: Major."
   }
  },
  {
   "id": "P037-N007",
   "work_id": "P037",
   "url": "https://pmaronow.github.io/llm-audit/works/P037/#P037-N007",
   "comment": "Agreed.",
   "suggestion": null,
   "classification_before_review": {
    "class": "error",
    "severity": "Moderate"
   },
   "classification_after_evaluation": {
    "class": "error",
    "severity": "Moderate"
   },
   "classification_now": {
    "class": "error",
    "severity": "Moderate"
   },
   "outcome": "no_change",
   "outcome_text": "No change was needed: my comment agrees with the entry.",
   "disagreement": null
  },
  {
   "id": "P037-N008",
   "work_id": "P037",
   "url": "https://pmaronow.github.io/llm-audit/works/P037/#P037-N008",
   "comment": "Agreed.",
   "suggestion": null,
   "classification_before_review": {
    "class": "error",
    "severity": "Moderate"
   },
   "classification_after_evaluation": {
    "class": "error",
    "severity": "Moderate"
   },
   "classification_now": {
    "class": "error",
    "severity": "Moderate"
   },
   "outcome": "no_change",
   "outcome_text": "No change was needed: my comment agrees with the entry.",
   "disagreement": null
  },
  {
   "id": "P037-N009",
   "work_id": "P037",
   "url": "https://pmaronow.github.io/llm-audit/works/P037/#P037-N009",
   "comment": "Agreed.",
   "suggestion": null,
   "classification_before_review": {
    "class": "error",
    "severity": "Moderate"
   },
   "classification_after_evaluation": {
    "class": "error",
    "severity": "Moderate"
   },
   "classification_now": {
    "class": "error",
    "severity": "Moderate"
   },
   "outcome": "no_change",
   "outcome_text": "No change was needed: my comment agrees with the entry.",
   "disagreement": null
  },
  {
   "id": "P037-N019",
   "work_id": "P037",
   "url": "https://pmaronow.github.io/llm-audit/works/P037/#P037-N019",
   "comment": "This is an important clarification.",
   "suggestion": null,
   "classification_before_review": {
    "class": "error",
    "severity": "Moderate"
   },
   "classification_after_evaluation": {
    "class": "error",
    "severity": "Moderate"
   },
   "classification_now": {
    "class": "error",
    "severity": "Moderate"
   },
   "outcome": "no_change",
   "outcome_text": "No change: my comment acknowledges the clarification's importance and adds no factual or mathematical correction; the classification is unchanged.",
   "disagreement": null
  },
  {
   "id": "P037-N025",
   "work_id": "P037",
   "url": "https://pmaronow.github.io/llm-audit/works/P037/#P037-N025",
   "comment": "Agreed.",
   "suggestion": null,
   "classification_before_review": {
    "class": "error",
    "severity": "Moderate"
   },
   "classification_after_evaluation": {
    "class": "error",
    "severity": "Moderate"
   },
   "classification_now": {
    "class": "error",
    "severity": "Moderate"
   },
   "outcome": "no_change",
   "outcome_text": "No change was needed: my comment agrees with the entry.",
   "disagreement": null
  },
  {
   "id": "P037-N026",
   "work_id": "P037",
   "url": "https://pmaronow.github.io/llm-audit/works/P037/#P037-N026",
   "comment": "Agreed.",
   "suggestion": null,
   "classification_before_review": {
    "class": "error",
    "severity": "Moderate"
   },
   "classification_after_evaluation": {
    "class": "error",
    "severity": "Moderate"
   },
   "classification_now": {
    "class": "error",
    "severity": "Moderate"
   },
   "outcome": "no_change",
   "outcome_text": "No change was needed: my comment agrees with the entry.",
   "disagreement": null
  },
  {
   "id": "P037-N032",
   "work_id": "P037",
   "url": "https://pmaronow.github.io/llm-audit/works/P037/#P037-N032",
   "comment": "I think this is a clarification point.",
   "suggestion": "Minor",
   "classification_before_review": {
    "class": "error",
    "severity": "Moderate"
   },
   "classification_after_evaluation": {
    "class": "error",
    "severity": "Moderate"
   },
   "classification_now": {
    "class": "error",
    "severity": "Moderate"
   },
   "outcome": "not_adopted",
   "outcome_text": "Not adopted: the entry stays a Moderate error, not the Minor rating I suggested.",
   "disagreement": {
    "models_position": "A competent reader expects directed nominations, and the symmetrization is not clear from context. The omission changes the exposure labels and the interpretation and reconstruction of every primary contrast within schools: Moderate."
   }
  },
  {
   "id": "P037-N033",
   "work_id": "P037",
   "url": "https://pmaronow.github.io/llm-audit/works/P037/#P037-N033",
   "comment": "Agreed.",
   "suggestion": "Moderate",
   "classification_before_review": {
    "class": "error",
    "severity": "Moderate"
   },
   "classification_after_evaluation": {
    "class": "error",
    "severity": "Moderate"
   },
   "classification_now": {
    "class": "error",
    "severity": "Moderate"
   },
   "outcome": "no_change",
   "outcome_text": "No change: I agreed with the entry and its Moderate rating.",
   "disagreement": null
  },
  {
   "id": "P040-E001",
   "work_id": "P040",
   "url": "https://pmaronow.github.io/llm-audit/works/P040/#P040-E001",
   "comment": "I agree that what is proposed is a better analysis, but this is a standard interpretation of a asymptotic Wald-type interval (even if the asymptotics weren't warranted).",
   "suggestion": "Minor",
   "classification_before_review": {
    "class": "error",
    "severity": "Moderate"
   },
   "classification_after_evaluation": {
    "class": "error",
    "severity": "Moderate"
   },
   "classification_now": {
    "class": "error",
    "severity": "Moderate"
   },
   "outcome": "incorporated_in_part",
   "outcome_text": "Incorporated in part. The claims that .81 cannot be a nominal 95% limit and that the paper misread its interval were withdrawn, and the entry was redrafted around the interval’s poor calibration in small samples. My suggested rating (Minor) was not adopted.",
   "disagreement": {
    "models_position": "For the reconstructed nominal 95% Wald interval, with 15 independent responses and a common detection probability of .80, two-sided coverage is only about 81.5%. The conventional reading of its lower endpoint was accepted, but the paper’s explicit assurance of at least 80% rests on a seriously inaccurate small-sample approximation: Moderate."
   }
  },
  {
   "id": "P041-E006",
   "work_id": "P041",
   "url": "https://pmaronow.github.io/llm-audit/works/P041/#P041-E006",
   "comment": "Agreed.",
   "suggestion": null,
   "classification_before_review": {
    "class": "error",
    "severity": "Major"
   },
   "classification_after_evaluation": {
    "class": "error",
    "severity": "Major"
   },
   "classification_now": {
    "class": "error",
    "severity": "Major"
   },
   "outcome": "no_change",
   "outcome_text": "No change was needed: my comment agrees with the entry.",
   "disagreement": null
  },
  {
   "id": "P043-E007",
   "work_id": "P043",
   "url": "https://pmaronow.github.io/llm-audit/works/P043/#P043-E007",
   "comment": "I think this is largely a subjective interpretation of wording.",
   "suggestion": "Minor",
   "classification_before_review": {
    "class": "error",
    "severity": "Moderate"
   },
   "classification_after_evaluation": {
    "class": "error",
    "severity": "Moderate"
   },
   "classification_now": {
    "class": "error",
    "severity": "Moderate"
   },
   "outcome": "not_adopted",
   "outcome_text": "Not adopted: the entry stays a Moderate error, not the Minor rating I suggested.",
   "disagreement": {
    "models_position": "The control group’s mean falls by about 0.09, around 10% of its starting level and 56% of the initial gap between the groups, so describing it as essentially unchanged materially changes how a reader understands the trajectories: Moderate."
   }
  },
  {
   "id": "P043-N014",
   "work_id": "P043",
   "url": "https://pmaronow.github.io/llm-audit/works/P043/#P043-N014",
   "comment": "I think the context implies the hypothesized sign of the effect that would make this claim true.",
   "suggestion": "clarity_note",
   "classification_before_review": {
    "class": "error",
    "severity": "Moderate"
   },
   "classification_after_evaluation": {
    "class": "not_confirmed",
    "severity": null
   },
   "classification_now": {
    "class": "not_confirmed",
    "severity": null
   },
   "outcome": "incorporated_in_part",
   "outcome_text": "Incorporated in part. The contextual reading was accepted, and the entry is now not confirmed, with no severity: under the classification rule, an allegation that is not confirmed is classed as not confirmed rather than as the clarity note I suggested.",
   "disagreement": {
    "models_position": "The contextual reading was accepted; under the classification rule the allegation is then not confirmed, so the entry is neither an error nor a clarity note."
   }
  },
  {
   "id": "P044-N005",
   "work_id": "P044",
   "url": "https://pmaronow.github.io/llm-audit/works/P044/#P044-N005",
   "comment": "I agree that this is an important practical problem. I think there's a bit of ambiguity in wording (the estimator is conservative, the Monte Carlo-based approximation doesn't have enough replicates in some applications). I think the answer is \"K=10000\" replicates is not sufficient for some applications, or some correction is needed to guarantee conservatism. This is not an unfamiliar problem (think, e.g., the bootstrap).\n\nComment after LLM decision: I don't know if I agree that this is a major error per se, but I do agree that it should be flagged, and I'm glad that this is getting attention.",
   "suggestion": "Moderate",
   "classification_before_review": {
    "class": "error",
    "severity": "Major"
   },
   "classification_after_evaluation": {
    "class": "error",
    "severity": "Major"
   },
   "classification_now": {
    "class": "error",
    "severity": "Major"
   },
   "outcome": "incorporated_in_part",
   "outcome_text": "Incorporated in part. The entry now gives the analogy with the bootstrap and names more simulation draws, with accuracy checks or proved error control, as a possible remedy. My suggested rating (Moderate) was not adopted.",
   "disagreement": {
    "models_position": "The Horvitz–Thompson variance estimator is conservative with exact exposure probabilities and positive marginal support. In a small-world network regenerated from the chapter’s printed code, the finite simulation recipe can severely understate variance when the realized assignment is independent of the simulated assignments, which undermines a central guarantee of conservativeness: Major. No published table entry is shown to be wrong."
   }
  },
  {
   "id": "P045-N016",
   "work_id": "P045",
   "url": "https://pmaronow.github.io/llm-audit/works/P045/#P045-N016",
   "comment": "I think it is clear that it is meant for the known pi case / large R case.",
   "suggestion": "Minor",
   "classification_before_review": {
    "class": "error",
    "severity": "Moderate"
   },
   "classification_after_evaluation": {
    "class": "clarity_note",
    "severity": null
   },
   "classification_now": {
    "class": "clarity_note",
    "severity": null
   },
   "outcome": "incorporated_in_part",
   "outcome_text": "Incorporated in part. The contextual reading was accepted, and the entry was reclassified from a Moderate error to a clarity note, which goes further than the Minor rating I suggested.",
   "disagreement": null
  },
  {
   "id": "P046-E001",
   "work_id": "P046",
   "url": "https://pmaronow.github.io/llm-audit/works/P046/#P046-E001",
   "comment": "Agreed.",
   "suggestion": null,
   "classification_before_review": {
    "class": "error",
    "severity": "Moderate"
   },
   "classification_after_evaluation": {
    "class": "error",
    "severity": "Moderate"
   },
   "classification_now": {
    "class": "error",
    "severity": "Moderate"
   },
   "outcome": "no_change",
   "outcome_text": "No change was needed: my comment agrees with the entry.",
   "disagreement": null
  },
  {
   "id": "P046-E002",
   "work_id": "P046",
   "url": "https://pmaronow.github.io/llm-audit/works/P046/#P046-E002",
   "comment": "Agreed.",
   "suggestion": null,
   "classification_before_review": {
    "class": "error",
    "severity": "Minor"
   },
   "classification_after_evaluation": {
    "class": "error",
    "severity": "Minor"
   },
   "classification_now": {
    "class": "error",
    "severity": "Minor"
   },
   "outcome": "no_change",
   "outcome_text": "No change was needed: my comment agrees with the entry.",
   "disagreement": null
  },
  {
   "id": "P046-E004",
   "work_id": "P046",
   "url": "https://pmaronow.github.io/llm-audit/works/P046/#P046-E004",
   "comment": "Yes, this is what we meant but it was a misstatement.",
   "suggestion": "Minor",
   "classification_before_review": {
    "class": "clarity_note",
    "severity": null
   },
   "classification_after_evaluation": {
    "class": "clarity_note",
    "severity": null
   },
   "classification_now": {
    "class": "clarity_note",
    "severity": null
   },
   "outcome": "not_adopted",
   "outcome_text": "Not adopted: my suggested rating (Minor) was not adopted; the claim is correct in context, so the entry stays a clarity note without an error severity.",
   "disagreement": {
    "models_position": "Read with Proposition 1 and the remedies discussed in the body, the abstract's intended claim is correct. Assumptions on the outcome regression alone also permit a uniformly consistent estimator; the correction makes that omitted route explicit. Under the classification rule this is a clarity note."
   }
  },
  {
   "id": "P054-N001",
   "work_id": "P054",
   "url": "https://pmaronow.github.io/llm-audit/works/P054/#P054-N001",
   "comment": "I agree that this is a preferable analysis. The original analysis was just performed via weighted least squares (just intercept-only regression in R) with classical standard errors. But it doesn't materially affect any conclusions, and so I think this is minor.",
   "suggestion": "Minor",
   "classification_before_review": {
    "class": "error",
    "severity": "Moderate"
   },
   "classification_after_evaluation": {
    "class": "error",
    "severity": "Moderate"
   },
   "classification_now": {
    "class": "error",
    "severity": "Moderate"
   },
   "outcome": "incorporated_in_part",
   "outcome_text": "Incorporated in part. The entry records my report that the original analysis used intercept-only weighted least squares in R with classical standard errors; this reproduces the rounded numbers but could not be independently identified as the original procedure. My suggested rating (Minor) was not adopted.",
   "disagreement": {
    "models_position": "Under the stated survey reconstruction, every interval widens substantially, and the interval for stricter laws no longer excludes 50% at the two-sided 95% level: Moderate."
   }
  },
  {
   "id": "P061-N019",
   "work_id": "P061",
   "url": "https://pmaronow.github.io/llm-audit/works/P061/#P061-N019",
   "comment": "I agree this was misstated. I'm not sure that it's moderate (maybe minor), but it should have simply referred to the conditions in https://academic.oup.com/biomet/article-abstract/111/1/1/7078801",
   "suggestion": null,
   "classification_before_review": {
    "class": "error",
    "severity": "Moderate"
   },
   "classification_after_evaluation": {
    "class": "error",
    "severity": "Moderate"
   },
   "classification_now": {
    "class": "error",
    "severity": "Moderate"
   },
   "outcome": "not_adopted",
   "outcome_text": "Not adopted: the entry stays a Moderate error, and a citation alone was not accepted as the repair.",
   "disagreement": {
    "models_position": "On the available preprint of the linked article, its local-dependence conditions do not ensure that the comparison is defined for general exposure mappings, or that AEED equals an ordinary assignment direct effect, and exact counterexamples persist under them, so a citation alone does not repair the sentence. The closing comparison can materially mislead readers who apply it: Moderate. The published journal version was not checked, and no error in the linked article’s theorem is alleged."
   }
  }
 ]
}