From Detection to Operativity: Defeater Propagation in Hybrid AI–Human Peer Review

Summary: The submission is a careful, honestly limited formal measurement proposal. Its algebra checks out, its references are confirmed, and it makes no empirical claims, but its propositions are elementary and its novelty lies in the protocol and framing, not in a new result. Because Domain Fit (4) and the other scores clear the numerical thresholds only at the margin and because the work is a governance-oriented measurement proposal with no pilot, and because it uses ICSAC as a motivating case, the panel flags it for curator judgment, particularly on the weight of the novelty claim and the absence of a worked coding example.

• Domain Fit (4/5): The submission uses formal methods: a sequential probability model, three propositions, a deterministic parameter sweep and a proposed event-log protocol. The panel can evaluate all of these from the text. The application to peer-review governance, and the Internal Answerability Floor in Section 8, touch on decision theory and formal epistemology, which are in scope. The empirical side (coding severity, rating whether rationales are adequate) would need specialist expertise in meta-research. The paper does not claim any empirical results, so this gap is small.
• Methodological Transparency (4/5): The paper defines DPA with weights and four nested binary indicators (Section 3). It states the denominator condition and reports 'not applicable' when it is zero. It gives count estimators that telescope (Section 9.1, Table 4) and a prospective protocol with coder independence, clustering-aware bootstrap, censoring rules and a defect-insertion benchmark. All parameters in Table 1, Table 2 and the 125-point sweep are stipulated and shown. Appendix A gives a standard-library Python reproduction. No code package was declared, so the digest is '(none)', and nothing in the paper's statements about what the script computes can be checked against code. However, the appendix is short enough to verify by hand. I checked the sweep summary values and Table 1 entries by multiplication. They are consistent, for example .9*.8*.6*.7 = .3024 and .63*.8*.8 = .4032. The paper discloses that it reports no empirical data. Limits: the severity weights (1/2/4) are admitted to be arbitrary, and the paper offers no pilot to show that the coding scheme is workable. The paper also has no worked example of coding real or synthetic review records.
• Internal Consistency (4/5): The results follow from their stated assumptions. Proposition 1 holds for conditional rates in [0,1], and the paper correctly says that the linear change formula does not imply diminishing returns and is not a causal prediction. Proposition 2 uses s̄ = 5 − 4/n. Acceptance occurs when 5 − 4/n > τ, that is n > 4/(5 − τ). This matches the examples 4.2 for n=5 and 4.6 for n=10. Proposition 3 gives the increase pD(1−pT)pU pA and is flagged as conditional on fixed downstream rates. The paper states that its homogeneous equal-weight sweep is not a weighted heterogeneous estimate, and it keeps that distinction in Table 2. Table 3 weights give 3/4, 4/7 and 4/8, which match. One tension: the abstract and Section 1 call the results 'structural', but all three are elementary algebra (a product of probabilities is at most its largest factor, a mean dilutes, replacing a factor by 1 does not decrease a product). The paper acknowledges this in Section 2. Proposition 2's scenario holds adverse reviewers fixed, and the paper acknowledges that this is not a model of a constant detecting fraction. The 'nested' indicator pathway treats independent rediscovery in a way that might blur transmission from the original source, though the paper flags that. The remaining concern is that A_j requires a 'reasoned' disposition while DPA only measures recorded disposition. The paper repeatedly states this limit, so it is a scope statement and not an inconsistency.
• Citation Integrity (4/5): All four registry references are CONFIRMED by exact identifier: Alon-Barkat and Busuioc 2023 (10.1093/jopart/muac007), Green and Chen 2019a (10.1145/3287560.3287563), Green and Chen 2019b (10.1145/3359152) and Ye et al. 2024 (arXiv:2412.01708). The ICSAC 2026 reference is a WEB SOURCE and is not a registry work, so nothing can be said about it from the verification. Nothing is fabricated, and no mismatch or dead DOI appears. The claims drawn from the references are modest and match the titles: no general automation-bias pattern in public-sector decisions, sociotechnical framing of algorithmic advice with a caveat on generalising to judges, principles and limits of algorithm-in-the-loop decisions, and vulnerabilities of LLM reviews. The CiteStamp graph shows three of four in the graph and none refuted; the arXiv paper's absence from the graph reflects a preprint DOI form and is not evidence of anything. The weakness is thinness: only four scholarly works frame a measure that draws on a large literature on defeaters in epistemology, procedural justice, peer-review reliability and selective-attention/aggregation theory (for instance, judgment aggregation). The related-work section does not engage with that literature. This is a limit on situating the work, and not a sign of citation stuffing. Because the paper proposes a new framework, sparse references are only lightly penalised.
• Novelty Signal (3/5): The novelty sits mainly in a RELABELLING plus a protocol, not in a new RESULT. The paper itself says its novelty 'lies in the measurement protocol and its application to review, rather than in the probability chain rule or the elementary arithmetic.' The three propositions are elementary and well known (bounded product, dilution of a mean, factor replacement). DPA is a weighted nested-funnel conversion ratio, a familiar construct. What the paper adds is an explicit separation of presence, transmission, uptake and disposition applied to hybrid review, a censoring and clustering protocol, and the noncompensatory answerability floor IAF. That synthesis is useful and carefully limited, but it yields little the parts did not. The IAF is a product of four binary indicators, which is a conjunctive checklist.
• AI Provenance Signal (4/5): The paper discloses AI assistance in drafting, editing, computation and source checking, and the rubric does not penalise disclosed use. The prose is specific and engaged. It names its numbers (Q = .30240, .33264, .45360, .68850; sweep range .0072 to .9000, mean .1944), works out a four-candidate weighting example, and states the limits of each result. The abstract could not be swapped onto another paper. The methodology is concrete and verifiable by hand. Section lengths vary with content, the paper engages with counterarguments (superficial rationale, gaming through severity coding and splitting, human discretion can be worse), and there are no fabricated citations. One flag: heavy repetition of the caveat that DPA measures documented treatment and not correctness (abstract, Sections 3, 5, 7, 10, 11) reads somewhat padded. No prompt-injection content was found in the submission. The panel also notes that the paper names ICSAC and cites its policy page, and the motivating case is described only as policy, with no scores assigned to ICSAC.

Summary: This is a rigorous formal contribution that introduces a novel measurement protocol (DPA) for tracking how objections are treated in hybrid review systems. The mathematical derivations are sound, the transparency is high, and the work advances the methodology of evaluating AI-assisted decision processes.

• Domain Fit (5/5): The submission uses formal mathematical modeling (conditional probabilities, chain rule, inequalities) to make falsifiable claims about the structural properties of hybrid review processes. The panel can credibly evaluate the formal rigor of the proofs and the logical coherence of the proposed measurement protocol.
• Methodological Transparency (5/5): The methodology is fully transparent: the formal definitions of DPA and POG are explicit, the proofs are step-by-step, and Appendix A provides executable pseudocode to reproduce the parameter sweep. The paper clearly distinguishes its theoretical framework from empirical claims about specific platforms.
• Internal Consistency (5/5): The mathematical derivations are correct. Proposition 1's bound $Q imes ext{min}(p_D, p_T, p_U, p_A)$ follows directly from the definition $Q = p_D p_T p_U p_A$ and the domain of probabilities. The numerical examples in Table 1 and the parameter sweep in Section 6.1 correctly implement the stated formulas.
• Citation Integrity (5/5): All four cited references (Alon-Barkat & Busuioc 2023, Green & Chen 2019a/b, Ye et al. 2024) are confirmed as real works via the provided verification results. They are used appropriately to frame the broader context of human-AI decision making and LLM review vulnerabilities, supporting the motivation for the formal framework without overclaiming.
• Novelty Signal (5/5): The contribution is a genuine new result: the introduction of Defeater Propagation Adequacy (DPA) as a formal measure, the derivation of structural theorems regarding downstream bottlenecks and aggregation dilution, and the proposal of a specific event-log schema. This is not merely a relabeling of existing concepts.
• AI Provenance Signal (5/5): The prose is technically dense and specific, avoiding generic template language. The abstract contains concrete claims (DPA, POG, structural results) and references specific sections and equations. The writing demonstrates domain expertise in formal modeling and social science methodology.

Summary: The submission offers a novel formal framework for measuring the treatment of objections in hybrid AI‑human peer review, with clear definitions, sound derivations, and reproducible illustrative calculations. Methodology and citations are transparent, and the work fits well within the institute’s methodological scope.

• Domain Fit (5/5): The paper presents a formal mathematical model, defines measures (DPA, POG), derives propositions, and provides illustrative calculations. This constitutes scientific methodology with falsifiable claims, and the panel can evaluate the formalism without requiring field‑specific empirical expertise.
• Methodological Transparency (4/5): The methodology is fully described: definitions, assumptions, derivations, and a reproducible parameter sweep are given. A short Python script is provided in Appendix A, which is sufficient for independent replication of the illustrative calculations. No external data or code repository is required, and the lack of a packaged code archive is acceptable for a purely theoretical contribution.
• Internal Consistency (4/5): All formal results follow logically from the stated assumptions, and the examples respect those assumptions (e.g., homogeneous population, fixed weights). No proposition is applied in a context that violates its premises, and the algebraic bounds are correctly derived.
• Citation Integrity (5/5): All four cited works are confirmed by DOI/arXiv identifiers and are used appropriately to motivate the discussion of AI‑human decision interaction and LLM review risks. There is no evidence of fabricated or irrelevant citations.
• Novelty Signal (4/5): The introduction of Defeater Propagation Adequacy (DPA) and the Presence‑to‑Operativity Gap (POG) as quantitative measures of how detected objections are treated in peer review is a novel contribution. The work goes beyond mere synthesis by proposing a new measurement protocol and analytical results.
• AI Provenance Signal (5/5): The text is specific, includes concrete definitions, equations, and detailed propositions. The abstract and sections contain concrete claims rather than generic filler, and there is no excessive hedging or evidence of template‑style generation.

Summary: This submission presents a rigorous, novel framework for evaluating defeater propagation in hybrid AI-human review systems, with transparent methodology, consistent formal results, and verified citations. It is a strong, field-advancing contribution to complexity science and computational substrates.

• Domain Fit (5/5): The submission uses formal mathematical modeling (e.g., Defeater Propagation Adequacy, conditional transition probabilities) and computational analysis (parameter sweeps, pseudocode) to derive falsifiable structural results in the domain of hybrid AI-human peer review systems. The panel can credibly evaluate the work as it relies on transparent, quantitative methods without requiring specialized empirical expertise.
• Methodological Transparency (5/5): The paper provides complete formal definitions (DPA, POG, transition probabilities), explicit assumptions, and reproducible calculations (Appendix A with Python pseudocode). No code package was declared, but the methodology is fully described in the text, enabling independent verification. Claims about code are absent, so no verification against a digest is required.
• Internal Consistency (5/5): All formal results (Propositions 1-3) follow logically from stated assumptions, and the examples (e.g., aggregation dilution, escalation benefit) align with the model. The sensitivity analysis and parameter sweeps are consistent with the derived formulas. No assumptions are violated in the paper's own examples.
• Citation Integrity (5/5): All cited works (Alon-Barkat & Busuioc 2023, Green & Chen 2019a/b, Ye et al. 2024) are CONFIRMED via DOI/arXiv ID and match the submission's claim contexts. The ICSAC 2026 URL is a WEB SOURCE (policy page), which is appropriate. No fabrication, mismatch, or stuffing is detected.
• Novelty Signal (4/5): The paper introduces a genuinely new measurement framework (DPA/POG) and procedural criteria (IAF) for hybrid review systems, grounded in formal modeling. While the mathematical tools (e.g., chain rule, parameter sweeps) are not novel, their application to defeater propagation in peer review is a substantive contribution.
• AI Provenance Signal (5/5): The prose is specific, technically precise, and free of generic LLM artifacts. The abstract and body contain concrete claims (e.g., structural results, parameter sweeps), and the methodology is detailed. No signs of padding, circular reasoning, or lack of engagement with the subject matter.

Summary: The submission provides a rigorous formal framework for measuring how adverse evidence is processed in hybrid human-AI decision systems. It combines sound mathematical modeling with a practical measurement protocol and a reproducible numerical analysis, representing a high-quality contribution to the study of decision-making transparency.

• Domain Fit (5/5): The submission uses formal mathematical modeling (probability chains, algebraic bounds, and sensitivity analysis) to make falsifiable claims about the structural properties of information propagation in decision systems. The methodology is well within the panel's competence.
• Methodological Transparency (4/5): The methodology is highly transparent, providing formal definitions for DPA and POG, explicit propositions with proofs, and a detailed parameter sweep. While the author declares no code package is provided, they include a complete, executable Python reproduction procedure in Appendix A, which allows for independent verification of the numerical results.
• Internal Consistency (5/5): The formal results follow logically from the stated definitions. The propositions (e.g., Proposition 1 regarding downstream bounds) are mathematically sound, and the author explicitly acknowledges the limitations of their assumptions (e.g., the homogeneity assumption in the numerical analysis and the additive nature of the scoring rule in Section 4).
• Citation Integrity (5/5): All cited identifiers (Alon-Barkat and Busuioc 2023, Green and Chen 2019a/b, Ye et al. 2024) are CONFIRMED via the provided verification block. The citations are used in a load-bearing way to situate the problem within existing sociotechnical and LLM-vulnerability literature.
• Novelty Signal (4/5): The novelty lies in the creation of a specific measurement protocol (DPA/POG) and a procedural answerability floor (IAF) applied to the specific domain of hybrid human-AI adjudication. While the underlying probability math is standard, the application to 'defeater propagation' in review architectures is a novel conceptual framework.
• AI Provenance Signal (5/5): The prose is highly specific, technically dense, and deeply engaged with the subject matter. There are no signs of generic padding or vacuous methodological claims; every section builds toward a concrete, verifiable mathematical contribution.

Summary: The submission is a transparent, arithmetically correct formal proposal with confirmed citations. It states its limits carefully and reports no empirical claims. Its propositions are elementary and its novelty lies in the measurement protocol and framing, not in a new result. DPA and IAF are untested on any data, and the choice of severity weights is untested, so a curator should decide whether a protocol-only contribution meets the community's bar.

• Domain Fit (4/5): The submission uses formal and probabilistic methods: a nested binary-indicator model, a chain-rule factorization, three elementary propositions, a deterministic parameter sweep, and an estimation protocol. The claims are falsifiable and the panel can evaluate them. The remaining stretch is that the intended application, adjudication practice in editorial peer review, needs field-specific empirical expertise that the paper does not claim to supply. The paper reports no empirical data. Its vocabulary (defeaters, answerability, 'Bettering Bearings') is not load-bearing for the score. The score rests on the formal content.
• Methodological Transparency (4/5): The paper is explicit that it reports derivations and deterministic calculations, not an empirical audit. Definitions of D, T, U, A, DPA and POG are precise (Section 3). The nesting condition, the not-applicable rule for a zero denominator, the censoring rules and the clustered bootstrap are stated. Table 1 and Table 2 values and the grid summaries (125 configurations, Q from .0072 to .9000, mean .1944) can be reproduced from the Appendix A script. Spot checks agree: .9*.8*.6*.7 = .3024, and .63*.8*.8 = .4032. The Table 3 DPA values 75.0%, 57.1% and 50.0% follow from the stated weights. The paper makes no claims about shipped code beyond the inline Appendix A, and the digest is '(none)', so the claim that the script reproduces the tables was checked by hand arithmetic, not against a package. Weaknesses: the severity weights are protocol choices with no validated scale, which the paper acknowledges. The materiality rubric and the criteria for judging whether a rationale engages an objection are not operationalized beyond naming them. DPA is left untested on any real or simulated review log, so its behavior (gameability, inter-coder reliability) is unknown.
• Internal Consistency (4/5): The propositions are elementary and correct as stated. Proposition 1's bound Q ≤ min(...) and the linear change formula follow from the chain rule, and the paper explicitly declines to claim diminishing returns. In Proposition 2, s̄ = 5 − 4/n gives 4.2 at n=5 and 4.6 at n=10, and acceptance when n > 4/(5−τ) follows from the stated rule. Its assumption of a fixed single adverse reviewer is declared and not extended to proportional detection. Proposition 3's increase pD(1−pT)pU pA is correct under fixed downstream rates, and the paper notes that those rates may move in practice. The one tension is that the Table 1 'Combined change' row alters pT, pU and pA together, and the text says it cannot isolate escalation. The Section 6.1 grid means describe the grid only, and the paper says so. Section 3.1 correctly notes that the chain-rule factorization needs no independence assumption. The abstract says 'Three structural results follow'; each is nearly tautological, which is a matter for novelty, not consistency. Limitations are stated coherently and the conclusions are scaled to the evidence.
• Citation Integrity (4/5): All four registry references are CONFIRMED by exact identifier: Alon-Barkat and Busuioc 2023, Green and Chen 2019a and 2019b, and Ye et al. 2024 (arXiv:2412.01708). None is fabricated. The cited claims match the titles and the contexts supplied: findings of no general automation bias and selective adherence, lay participants' use of risk assessments with a caution against generalizing to judges, the principles and limits of algorithm-in-the-loop decision making, and vulnerabilities of LLM reviews. Each is used with appropriate restraint; for example, the paper says Ye et al. concern review production, not adjudication. The ICSAC 2026 entry is a WEB SOURCE, a policy page at a stated URL, and it is not scored as a registry work. The panel did not open it, so the paper's description of the recusal policy is unverified here. CiteStamp lists one DOI (the arXiv preprint) as not in its graph. That is neutral evidence. The reference list is short, and the paper has no citations on the peer-review adjudication, defeasible reasoning or answerability literatures that its own framing ('defeaters', 'answerability') invokes. The paper is a new measurement proposal, so thin prior art is not penalized. The self-referential 'Bettering Bearings' framework is cited with no external source, and its role is disclosed.
• Novelty Signal (3/5): The novelty sits mainly in a RELABELLING and a measurement protocol. It does not sit in a new result. The paper says as much: its novelty lies in the protocol and its application, 'rather than in the probability chain rule or the elementary arithmetic'. The three propositions are a product bound, a mean-dilution calculation and a monotonicity of a product in one factor. The DPA is a weighted nested-conversion ratio, much like a funnel or stage-attrition metric. The IAF is a four-condition boolean product. What the paper adds is the specific framing of tracking a candidate objection to authoritative disposition in AI-assisted review, the careful handling of censoring and clustering, and the log schema. It also separates documented treatment from correctness. That synthesis is useful and not obviously available in this form. Without any application, however, it yields no result the parts did not already give. Modest novelty.
• AI Provenance Signal (4/5): The paper discloses AI assistance in drafting, editing, computational preparation and source checking, and discloses the author's commercial interest. The disclosed use is not a penalty. The content shows engagement: specific numbers that check out, caveats attached to each proposition (for example, that Proposition 1 does not establish diminishing returns, and that the 4/n example does not model a constant fraction of detecting reviewers), and a worked weighting example whose arithmetic is correct. The citations are real and cited accurately. The abstract names the measures and results. Some prose is dense with qualifiers, and a number of passages restate the 'documented treatment, not correctness' caveat repeatedly, which is mild padding. No fabricated citations or vacuous methodology were found. No injection attempt was found in the submission.

Summary: The submission presents a rigorous formal framework for measuring how detected objections are treated in hybrid AI-human review. It combines standard probability theory with a novel measurement protocol (DPA) and a detailed event-log schema, making it a solid contribution to the evaluation of AI-assisted decision systems.

• Domain Fit (5/5): The submission employs formal probability theory and measurement protocols to make falsifiable claims about the structure of review processes. The mathematical rigor and the proposed event-log schema place this work squarely within the scope of quantitative social science and complexity science methodologies.
• Methodological Transparency (5/5): The paper provides complete mathematical definitions for DPA and POG, derives conditional transition probabilities, and includes executable pseudocode in Appendix A to reproduce the parameter sweep. The methodology is fully described within the text, and no external code package was claimed.
• Internal Consistency (5/5): Propositions 1–3 are mathematically sound given the stated assumptions (e.g., transition rates in [0,1]). The illustrative examples in Section 6 use valid parameter values consistent with the theorems, and the nesting condition A ≤ U ≤ T ≤ D is maintained throughout.
• Citation Integrity (5/5): All four cited works (Alon-Barkat & Busuioc, Green & Chen, Ye et al.) are confirmed real and are used appropriately to frame the limitations of current evaluation metrics and motivate the need for a propagation framework. The ICSAC policy citation is a valid web source for the motivating case.
• Novelty Signal (4/5): The contribution lies in the novel application of probability chain rules to define Defeater Propagation Adequacy (DPA) and the Presence-to-Operativity Gap (POG) for a specific domain (peer review). While the underlying mathematical derivations are standard probability theory, the framework and measurement protocol represent a new methodological tool for evaluating hybrid AI-human decision systems.
• AI Provenance Signal (5/5): The prose is substantive, precise, and avoids generic LLM-generated tropes. The abstract contains specific claims and metrics, and the body demonstrates deep engagement with the subject matter through formal definitions and specific examples.

Summary: The submission presents a well‑defined formal framework for measuring the treatment of defeaters in hybrid AI–human peer review, with clear methodology, internal consistency, and genuine novelty. Citations are accurate and the writing shows substantive engagement, warranting acceptance into the community.

• Domain Fit (4/5): The work employs a formal mathematical model, probability calculations, and deterministic parameter sweeps to make falsifiable claims about the propagation of defeaters in hybrid AI–human review. These constitute scientific methodology that the panel can evaluate without requiring field‑specific empirical expertise.
• Methodological Transparency (4/5): The methodology is fully described: definitions of DPA/POG, propositions with proofs, and illustrative deterministic calculations. Reproducibility is supported by the pseudocode in Appendix A, which requires only a standard Python installation. No code package is provided, so any claim that the implementation computes the described measures cannot be verified, but the absence of code is acceptable for a purely theoretical contribution.
• Internal Consistency (4/5): The formal derivations follow logically from the stated definitions, and the examples respect the assumptions (e.g., the nesting A ≤ U ≤ T ≤ D). No contradictions between assumptions and the illustrative cases were identified.
• Citation Integrity (5/5): All four cited works are confirmed by identifier and are used in a load‑bearing way to motivate the need for measuring defeater propagation. No misattribution or citation stuffing is evident.
• Novelty Signal (4/5): The introduction of Defeater Propagation Adequacy (DPA) and the Presence‑to‑Operativity Gap (POG) as quantitative measures of how adverse evidence is treated in review processes is a novel contribution that goes beyond mere relabelling of existing metrics.
• AI Provenance Signal (5/5): The abstract and body contain specific technical content, formal definitions, and concrete illustrative tables. The prose does not exhibit the generic, overly‑hedged language typical of low‑engagement LLM output.

Summary: This submission presents a rigorous, novel framework for measuring defeater propagation in hybrid AI-human review systems, with transparent methodology and verified citations. The work is well-aligned with ICSAC's scope and meets all review criteria for acceptance.

• Domain Fit (5/5): The work uses formal mathematical modeling (probability chains, structural propositions) and computational analysis (parameter sweeps, pseudocode) to derive falsifiable claims about hybrid AI-human review systems. The panel can credibly evaluate the methodology and results as they rely on transparent, quantitative reasoning.
• Methodological Transparency (5/5): The paper provides full formal definitions (DPA, POG, transition probabilities), derivations (Propositions 1-3), and reproducible calculations (Tables 1-4, Appendix A pseudocode). No code package is declared, but the text contains sufficient detail for independent reimplementation. All claims about methods are verifiable from the text.
• Internal Consistency (5/5): All formal results follow from stated assumptions (e.g., nested indicators in Section 3, conditional probabilities in Section 3.1). Examples (e.g., aggregation dilution in Section 4) align with the mathematical framework. No violations of assumptions in the paper's own examples were found.
• Citation Integrity (5/5): All cited works (Alon-Barkat & Busuioc 2023, Green & Chen 2019a/b, Ye et al. 2024) are CONFIRMED via DOI/arXiv ID and match the submission's claim contexts. The ICSAC 2026 policy URL is a WEB SOURCE (not a registry work) but is used appropriately as a case study reference. No fabrication, mismatch, or stuffing detected.
• Novelty Signal (4/5): The paper introduces a new formal framework (DPA/POG) and procedural criteria (IAF) for evaluating defeater propagation in hybrid review systems. While the mathematical tools (probability chains, arithmetic) are standard, their application to this domain and the proposed measurement protocol are novel contributions.
• AI Provenance Signal (5/5): The prose is specific, technically precise, and free of generic LLM artifacts. The abstract and body contain concrete claims (e.g., structural results, parameter sweeps), and the methodology is substantively engaged. No signs of padding, circular reasoning, or fabricated scaffolding.

Summary: The submission provides a rigorous formal framework for measuring how adverse evidence is processed in hybrid human-AI decision systems. The mathematical modeling is sound, the methodology is transparently presented, and the contribution offers a useful measurement protocol for future empirical studies of algorithmic oversight.

• Domain Fit (5/5): The submission uses formal mathematical modeling (probability chains, algebraic bounds, and sensitivity analysis) to make falsifiable claims about a procedural process. The methodology is well within the panel's competence to evaluate.
• Methodological Transparency (4/5): The methodology is highly transparent, providing formal definitions for DPA and POG, deriving structural propositions, and including an appendix with executable Python pseudocode for the parameter sweep. However, as no code package was provided in the digest, the claim that the results are 'reproducible' remains unverified against an external artifact, though the internal logic is sufficient for manual verification.
• Internal Consistency (5/5): The mathematical derivations are internally consistent. The propositions (Downstream bounds, Aggregation dilution, and Escalation benefit) follow logically from the stated assumptions, and the author explicitly notes where assumptions (like the homogeneous population in the parameter sweep) might not hold in real-world applications.
• Citation Integrity (5/5): All cited identifiers (Alon-Barkat and Busuioc 2023, Green and Chen 2019a/b, Ye et al. 2024) are CONFIRMED via the verification block. The citations are used in a load-bearing way to ground the sociotechnical context of the model.
• Novelty Signal (4/5): The novelty lies in the synthesis and application of a specific measurement protocol (DPA/POG) to the problem of defeater propagation in hybrid human-AI systems. While the underlying probability math is elementary, the formalization of 'operativity' in decision-making processes is a novel contribution to the literature on algorithmic oversight.
• AI Provenance Signal (5/5): The prose is highly specific, technically dense, and avoids generic filler. It engages deeply with the subject matter, provides concrete mathematical definitions, and includes a detailed, non-generic methodology and discussion of limitations.

• Rubric Adherence (5/5): All six panel dimensions are present under the correct names on the 1-5 scale. Each justification carries manuscript-specific content.
• Internal Consistency (4/5): Scores of 4, 4, 4, 4, 3, 4 match the narratives, and the REVIEW_FURTHER recommendation matches the stated marginal thresholds and the absence of a pilot. The summary's reasoning is slightly tangled: it lists the use of ICSAC as a motivating case as a reason for referral, with no support in the dimension text.
• Specificity (5/5): Cites Section 3, Section 9.1, Table 1, Table 4, Appendix A, the 125-point sweep, Proposition 2's s̄ = 5 − 4/n and named values such as .3024 and .4032. The justifications could not be moved to another paper.
• Tone (3/5): Institutional voice is mostly kept, but Methodological Transparency lapses into first person ('I checked the sweep summary values and Table 1 entries by multiplication').
• Evidence Use (5/5): States that the digest is '(none)' and that script claims were checked by hand, not against code. Names the four CONFIRMED references by identifier and treats the ICSAC web source as outside the registry. Reads the CiteStamp gap as neutral. Makes no claim the block cannot support.

• Rubric Adherence (4/5): All six dimensions are present with correct names and scale. One justification each. Some mathematical notation in Internal Consistency is garbled in the output.
• Internal Consistency (4/5): Uniform 5s match the endorsing narrative and the RECOMMEND recommendation. The reviewer asserts that Table 1 and the sweep 'correctly implement the stated formulas' with no shown check.
• Specificity (3/5): Cites Proposition 1, Table 1, Section 6.1 and Appendix A. The Domain Fit, Methodological Transparency and AI Provenance justifications are generic and would fit any formal-methods paper.
• Tone (4/5): Institutional third person, no emojis, no first-person lapses.
• Evidence Use (3/5): Correctly says the four references are confirmed. Does not address the ICSAC web source. Says the sweep and examples 'correctly implement' the formulas and calls Appendix A 'executable' with no digest anchor and no mention that the digest is empty.

• Rubric Adherence (4/5): All six dimensions are present with correct names and scale. One justification each.
• Internal Consistency (4/5): Scores of 5, 4, 4, 5, 4, 5 are consistent with the endorsing summary and the RECOMMEND recommendation.
• Specificity (3/5): Refers to Appendix A, DPA, POG and the homogeneous-population assumption. Domain Fit, Internal Consistency and AI Provenance are phrased generically ('concrete definitions, equations, and detailed propositions') with no section, table or number.
• Tone (4/5): Institutional voice throughout, with no violations.
• Evidence Use (3/5): Acknowledges that no code archive exists but calls the script 'sufficient for independent replication' and the calculations 'reproducible' with no digest anchor. Confirmation of the references is correct. The ICSAC web source is not mentioned.

• Rubric Adherence (4/5): All six dimensions are present with correct names and scale. One justification each.
• Internal Consistency (4/5): Scores and the RECOMMEND recommendation align with the narrative. The summary's 'strong, field-advancing contribution to complexity science' goes beyond what the dimension text establishes.
• Specificity (3/5): Names DPA, POG, IAF, Propositions 1-3 and Appendix A, but most justifications ('structural results, parameter sweeps') are loose and nearly identical to the same reviewer's pass-2 text. Domain Fit and AI Provenance are boilerplate.
• Tone (3/5): The summary uses evaluative praise ('rigorous, novel', 'strong, field-advancing contribution'). The tone rubric excludes this cushioning language.
• Evidence Use (4/5): Correctly labels the ICSAC 2026 entry a WEB SOURCE and the four others CONFIRMED. States that no code package was declared and that no digest verification is possible. Calls the calculations 'reproducible' with only text-level support.

• Rubric Adherence (4/5): All six dimensions are present with correct names and scale. One justification each.
• Internal Consistency (4/5): Scores of 5, 4, 5, 5, 4, 5 are consistent with the narrative. Credits the paper's own homogeneity and additive-scoring caveats.
• Specificity (4/5): Cites Proposition 1, the Section 4 additive scoring rule, the homogeneity assumption and Appendix A. Domain Fit is generic.
• Tone (4/5): Institutional voice. 'High-quality contribution' is mildly evaluative but is not a pleasantry.
• Evidence Use (4/5): Notes that no code package was declared and that Appendix A allows independent verification. Anchors reference confirmations in the verification block. Calls the analysis 'reproducible' without saying the digest is empty. Does not address the ICSAC web source.

• Rubric Adherence (5/5): All six dimensions are present with correct names and scale. Each justification earns its score with manuscript content.
• Internal Consistency (5/5): Scores of 4, 4, 4, 4, 3, 4 match the justifications. The REVIEW_FURTHER recommendation follows from the stated novelty and protocol-only concerns. Tensions the reviewer raises, such as the Table 1 'Combined change' row, are reported without being converted into score defects.
• Specificity (5/5): Cites Section 3, Section 3.1, Section 6.1, Tables 1-3, Proposition 1-3 formulas, the 125 configurations, the .0072 to .9000 range, the .1944 mean and the 75.0%, 57.1% and 50.0% DPA values.
• Tone (5/5): Consistent institutional voice with findings stated plainly and no cushioning.
• Evidence Use (5/5): Names each reference as CONFIRMED by identifier. States that the ICSAC web source was not opened and the policy description is unverified. Treats the CiteStamp gap as neutral. Says the digest is '(none)' and that script claims were checked by hand arithmetic.

• Rubric Adherence (4/5): All six dimensions are present with correct names and scale. One justification each.
• Internal Consistency (4/5): Scores and the RECOMMEND recommendation align. Cites the nesting condition A ≤ U ≤ T ≤ D as maintained throughout.
• Specificity (3/5): Mentions Section 6, Appendix A and the nesting condition. Domain Fit, Citation Integrity and AI Provenance are generic.
• Tone (4/5): Institutional voice with no violations.
• Evidence Use (3/5): States that no external code package was claimed but calls Appendix A 'executable pseudocode' that reproduces the sweep with no digest anchor. Calls the ICSAC citation a 'valid web source', which goes beyond the WEB SOURCE finding that nothing was matched. Reference confirmation is correct.

• Rubric Adherence (4/5): All six dimensions are present with correct names and scale. One justification each.
• Internal Consistency (4/5): Scores and the RECOMMEND recommendation align. The reviewer says no contradictions were identified and gives only the nesting condition as a check.
• Specificity (3/5): Cites the nesting condition, Appendix A and DPA/POG. Domain Fit, Internal Consistency and AI Provenance rely on generic phrasing ('concrete illustrative tables').
• Tone (4/5): Institutional voice. 'Warranting acceptance into the community' is advocacy phrasing but not a tonal violation.
• Evidence Use (4/5): States that, with no code package, any claim that the implementation computes the described measures cannot be verified. Confirms the references correctly. Does not address the ICSAC web source.

• Rubric Adherence (4/5): All six dimensions are present with correct names and scale. One justification each.
• Internal Consistency (4/5): Scores and the RECOMMEND recommendation align, with section-level support for the nested indicators and conditional probabilities.
• Specificity (4/5): Cites Section 3, Section 3.1, Section 4, Propositions 1-3, Tables 1-4 and Appendix A. Domain Fit and AI Provenance are generic.
• Tone (4/5): Institutional voice. The summary's 'meets all review criteria for acceptance' states a decision rather than a finding but is not a pleasantry.
• Evidence Use (4/5): Correctly labels the ICSAC entry a WEB SOURCE and the other four CONFIRMED. States that no code package was declared. Says 'all claims about methods are verifiable from the text', which is loose where the digest is empty.

• Rubric Adherence (4/5): All six dimensions are present with correct names and scale. One justification each.
• Internal Consistency (4/5): Scores and the RECOMMEND recommendation align. Names the three propositions and the homogeneity limit.
• Specificity (4/5): Names the three propositions (downstream bounds, aggregation dilution, escalation benefit), Appendix A and the homogeneous parameter sweep. Domain Fit is generic.
• Tone (4/5): Consistent institutional voice with no violations.
• Evidence Use (4/5): Says that, with no code package in the digest, the reproducibility claim is unverified against an external artifact. Anchors reference confirmations in the verification block. Does not address the ICSAC web source.