Reviewer Influence Effects

• Reviewer Influence Effects are the mechanisms by which reviewers’ identities, experiences, and report characteristics directly and indirectly shape paper outcomes and citation impacts.
• Quantitative methods, such as robust regression, network analysis, and agent-based simulations, effectively isolate factors like report length, sentiment, and reviewer network position.
• Implications for scholarly practice include the design of fairer review systems through targeted interventions like double-blind reviews, improved assignment algorithms, and enhanced reviewer training.

Reviewer influence effects denote the diverse mechanisms by which reviewers’ identities, experience, report characteristics, network positions, biases, and interactions shape scholarly outcomes within peer review and code review systems. These effects encompass direct impacts—such as changes in manuscript fate, citation impact, or review content—and more subtle forms, such as network-driven consensus, latent bias, or feedback on downstream scientific communication. Recent advances in experimental design, large-scale quantitative analysis, and agent-based simulation have made it possible to isolate, model, and measure these influences with increasing precision.

1. Typology of Reviewer Influence Effects

Reviewer influence manifests via multiple, empirically distinct vectors:

• Report Characteristics: The linguistic and structural properties of review reports, especially report length, sentiment, and substantive detail, have measurable impact on subsequent paper quality and visibility. In a robust analysis of 57,482 Web of Science publications, reviewer reports of at least 947 words independently predicted an 8%–16% increase in citation impact, conditional on journal and collaboration covariates (Maddi et al., 7 Mar 2024). Length functions as a proxy for substantive engagement, with longer reports indicating more requested improvements and greater downstream visibility.
• Reviewer Disposition and Interactions: Mean scores, score variance, and dominant sentiment—all reviewer-level aggregates—are potent acceptance predictors (Jung et al., 30 Sep 2025). Empirical models show that negative sentiment substantially reduces acceptance odds (OR ≈ 0.58), whereas positive sentiment modestly raises it (OR ≈ 1.13). High inter-reviewer score variance lowers acceptance, except when a few high scores rescue otherwise low-rated submissions.
• Co-reviewer Influence and Score Convergence: During the review-rebuttal cycle (e.g., ICLR 2024/2025), reviewer opinions systematically converge after discussion and author response. Score “disagreement” between reviewers contracts by 9%–10% overall and over 40% for papers in spotlight/oral tracks (Kargaran et al., 19 Nov 2025). Logistic regression establishes that the mean co-reviewer score is a significant predictor of score change: reviewers whose initial rating deviates from the group tend to update toward the mean.
• Review Network Position and Social Structure: The structural location of reviewers within reviewer-reviewer or co-authorship networks robustly predicts paper fate. In a 28k-paper paper, features such as reviewer degree, betweenness, and PageRank in the reviewer interaction network accounted for up to 79% of the variance in long-term citation count (Sikdar et al., 2017). Central reviewers, bridging disparate editor groups, are associated with higher-impact papers.
• Reviewer Experience and Expertise: In code review, empirical weighting of model training examples by reviewer experience (authoring code ownership, number of past reviews) with experience-aware loss functions dramatically improves the informativeness, relevance, and semantic depth of automated review comments (Lin et al., 17 Sep 2024).
• Intrinsic and Extrinsic Biases: Simulations and empirical audits have documented authority bias, nepotism, seniority/gender/geography bias, and citation bias. Double-blind protocols attenuate but do not fully eradicate these influences (Tvrznikova, 2018, Teplitskiy et al., 2018, Stelmakh et al., 2022). An agent-based model estimates that 30%–40% prevalence of self-interested or unqualified reviewers renders peer selection indistinguishable from random (Thurner et al., 2010, Jin et al., 18 Jun 2024).

2. Quantitative Measurement and Modeling Approaches

Advances in modeling peer review have enabled the quantification of reviewer influence at multiple system layers:

a. Regression and Panel Models

• Robust OLS: Used to estimate effects of review length on citations, with controls for journal impact, collaboration, funding, discipline, and publication year. Significant thresholds (e.g., 947 words) are identified via Fisher discretization (Maddi et al., 7 Mar 2024).
• Logistic Regression: Acceptance probability modeled as a function of mean reviewer scores, score variance, confidence, and sentiment (Jung et al., 30 Sep 2025).
• Difference-in-Differences Designs: Isolate the causal effect of review-exposure on subsequent citation behavior, leveraging natural experiments (e.g., declined reviewer invitations as exposure, weekend assignment as an instrument) (Wang et al., 16 Jul 2025).

b. Network Science

• Reviewer-Reviewer Interaction Networks: Node degree, clustering, betweenness, closeness, and PageRank of reviewers serve as predictive covariates. Regression against long-term citation quantifies the predictive value of network position (Sikdar et al., 2017).

c. Simulation and Agent-Based Models

• AgentReview LLM-based Simulation: Reviewer biases (social influence, altruism fatigue, authority, anchoring) are parameterized in reviewer-agent prompt tokens. Systematic variation in bias parameters yields 37.1% decision set variation; social influence alone flips decisions in 21.5% of cases (Jin et al., 18 Jun 2024).
• Analytical Agent Models: Population-based simulations demonstrate that as the fraction of rational (self-blocking) referees grows, average accepted-paper quality declines sharply, with a hard threshold near 30% (Thurner et al., 2010).

3. Documented Biases, Subjectivity, and Inter-Reviewer Dynamics

Peer review is susceptible to persistent forms of subjectivity and bias:

Bias Type	Quantitative Estimate / Detection	Key Reference
Social influence	27.2% SD reduction; 21.5% flips	(Jin et al., 18 Jun 2024)
Authority bias	Up to 27.7% decision flips	(Jin et al., 18 Jun 2024)
Co-reviewer proximity	–0.11 points/step favor; 3x higher rejection at distal nodes	(Teplitskiy et al., 2018)
Citation bias	+0.23 points (5-pt scale); +11% acceptance rank/pt	(Stelmakh et al., 2022)
Reviewer subjectivity	50% of quality-score variance	(Cortes et al., 2021)

Subjective reviewer variance remains high. The NeurIPS 2014 experiment attributes half of review-score variance to “subjectivity,” confirmed by Gaussian decomposition (σ²/(α_f+σ²) ≈ 0.50) (Cortes et al., 2021). Elongated reviews, even without added substance, gain +0.56 points in external evaluation on a 7-point scale; inter-rater disagreement rates for review quality assessments hover at 28–32% (Goldberg et al., 2023).

Peer-pressure, unwarranted consensus, and score anchoring are pervasive. However, randomized controlled trials that manipulate discussion order (e.g., “herding” via first-speaker positivity) do not always translate into significant acceptance-rate effects, suggesting compensating group-level mechanisms (Stelmakh et al., 2020).

4. Impact on Scholarship, Knowledge Diffusion, and System Design

Reviewer influence extends beyond immediate paper fate, shaping long-term scholarly trajectories and scientific idea flow:

• Citation and Visibility: Longer, more thorough reviews lead to subsequent articles receiving 8–16% more citations, independent of journal impact or collaboration (Maddi et al., 7 Mar 2024). Code reviews reflecting higher reviewer experience produce more actionable, detailed feedback (Lin et al., 17 Sep 2024).
• Idea Diffusion: Even declined reviewer invitees who glimpse only titles and abstracts subsequently increase their citation breadth, depth, and diversity to the submitting author by 13–23%; this process, at scale, positions peer review as an unrecognized engine of idea diffusion (Wang et al., 16 Jul 2025).
• System Design: Reviewer influence drives calls for more accountable, diversified, and transparent review systems. Interventions include double-blind and open review, explicit score-calibration and consensus protocols, reviewer network diversity mandates, and reviewer-assignment strategies designed to minimize bystander effects and balance workloads (Jin et al., 18 Jun 2024, Rigby et al., 2023).

5. Mitigation Strategies and Recommendations

A range of technical and procedural interventions are informed by the above findings:

• Assignment Algorithms: Utilize reviewer network metrics and experience signals to match papers, optimizing for impact and fairness (Sikdar et al., 2017, Lin et al., 17 Sep 2024, Rigby et al., 2023).
• Blinding and Transparency: Adopting double-blind review reduces authority and nepotism bias; open reports may incentivize more detailed, substantive reviewing (Tvrznikova, 2018, Maddi et al., 7 Mar 2024, Jin et al., 18 Jun 2024).
• Meta-Review and Oversight: Monitor score variance and sentiment distribution; take corrective action when high disagreement or outlier sentiment is noted (Jung et al., 30 Sep 2025).
• Reviewer and Author Training: Encourage constructive, well-evidenced reviews and effective author rebuttals; train evaluators to discount superficial verbosity (Maddi et al., 7 Mar 2024, Jung et al., 30 Sep 2025, Goldberg et al., 2023).
• Simulation-Aided Protocol Design: Use agent-based simulation frameworks to test policy variations for their expected impact on bias, consensus, and paper quality selection (Jin et al., 18 Jun 2024).
• Diversity of Epistemic Perspectives: Select reviewers across multiple network communities to mitigate “schools of thought” bias and increase contestation of validity (Teplitskiy et al., 2018).

Failure to maintain a predominantly “correct” referee pool (<30% self-interested or unqualified) critically undermines the selective power of peer review (Thurner et al., 2010). Proper balancing, calibration, and training are required to preserve the system’s intended function.

6. Limitations and Directions for Further Research

Several structural, methodological, and data limitations temper generalizability:

• Volunteer-based peer review platforms (e.g., Publons) may over-represent high-quality or OA-friendly reports, skewing effect-size estimates (Maddi et al., 7 Mar 2024).
• Many studies are necessarily restricted to accepted papers, lacking visibility into the fate or review dynamics of rejected manuscripts, thus potentially confounding causal inference.
• Observational designs risk unaccounted confounding, even with careful matching and regression controls; instrumental variable designs are advocated to resolve endogeneity (Wang et al., 16 Jul 2025, Maddi et al., 7 Mar 2024).
• Review process interventions (length, sentiment, assignment) show context dependence; randomized controlled trials remain the gold standard but are rare and logistically challenging (Stelmakh et al., 2020, Goldberg et al., 2023).
• Research on the downstream effects of reviewer bias on career advancement, research agendas, and field evolution remains limited.

Recent proposals therefore call for expanded experimental designs (including intervention in open-peer-review journals, process-level difference-in-differences, and augmented text analysis) to further clarify when, how, and for whom reviewer influence is beneficial, neutral, or detrimental (Maddi et al., 7 Mar 2024, Jung et al., 30 Sep 2025, Kargaran et al., 19 Nov 2025).