{"id":"60157297-4042-417c-bd05-7775a1709b0e","arxiv_id":"2607.00546","paper_version":1,"verdict":"UNVERDICTED","confidence":"LOW","novelty_score":4.0,"correctness_risk":"unknown","formal_verification":"none","parameter_count":0,"one_line_summary":"Regression analysis of 53k astrophysics papers finds open data, open access, and open code each linked to higher citation counts after controlling for grants, authors, length, date, and subfield.","lead":"This paper uses regression on 53,194 astrophysics papers to measure citation gains from open data (+32%), open access (+26%), and open code (+16%) after statistical controls. Smart readers might examine it for evidence on whether openness produces measurable personal returns in citations.","discovery_kind":"new_application","skeptic_critique":{"model":"grok-4.3","headline":"Regression controls omit direct paper-quality measures, risking omitted-variable bias in openness-citation link","rationale":"The concern matches the reader's weakest assumption exactly. The full-text methods section (regression specification and controls) does not add quality instrumentation or matching, so the isolation claim remains the load-bearing step. This moves the verdict from UNVERDICTED to CONDITIONAL pending the robustness check.","tokens_in":1855,"tokens_out":286,"duration_ms":16152,"concrete_test":"Re-estimate the main multivariate regression after adding a quality proxy (journal impact factor or reference count) as an extra covariate; if any openness coefficient shifts by >15% or loses significance at p<0.01, the reported advantages are sensitive to omitted quality.","verdict_should_be":"CONDITIONAL","load_bearing_attack":"The model controls for grants, authors, length, date, subfield, code size, language, and repo size, then reports +32% / +26% / +16% advantages via least-squares, partial correlations, and non-parametrics. No proxy for intrinsic quality (e.g., novelty, rigor, or visibility) is included. Openness decisions and citation rates can both be driven by unmeasured quality or subfield norms not captured by the keyword-based subfield split, so the coefficients may partly reflect selection rather than the effect of openness itself.","agreement_with_reader":"agree"},"referee_report":{"model":"grok-4.3","summary":"The paper analyzes citation benefits associated with open access, open data, and open code using a sample of 53,194 peer-reviewed astrophysics papers (2021–2025) from NASA's ADS. It measures eleven variables including openness indicators, grants, authors, length, date, subfield (via keywords: Solar System, Planet, Stellar, ISM, High Energy, Galaxies+Cosmology), code size, language, and repo size. A multivariate least-squares regression is tuned alongside partial correlations and non-parametric tests to isolate openness effects after controls, yielding reported advantages of +32% (open data, p<10^{-24}), +26% (open access, p<10^{-67}), and +16% (open code, p=0.003), with subfield variations and higher sharing rates in Galaxies+Cosmology and HEA.","tokens_in":1971,"tokens_out":556,"duration_ms":20676,"significance":"If the regression successfully isolates causal effects, the work supplies large-sample quantitative evidence that openness confers measurable citation gains in astrophysics, strongest for data and varying by subfield infrastructure. The combination of regression, partial correlations, and non-parametrics plus the sample size are methodological strengths that could inform open-science policy if robustness to selection is demonstrated.","major_comments":[{"comment":"The multivariate least-squares regression (described in the abstract and methods) controls for grants, authors, length, date, subfield, code size, language, and repo size but includes no proxy for intrinsic paper quality, novelty, or rigor. This omission risks confounding the reported +32%/+26%/+16% coefficients, as openness decisions and citation rates may both be driven by unmeasured quality; the central claim that controls isolate the contribution of openness therefore requires additional justification or sensitivity tests.","section":"Abstract and Methods (regression specification)"},{"comment":"Subfield classification is performed via keywords, yet the abstract notes that low sharing rates in Solar System and ISM partly reflect platforms not captured by the study. This raises the possibility that subfield-specific citation norms or data practices are incompletely controlled, which could affect the claim that the open-data advantage is present (and strongest) in all six sub-fields.","section":"Abstract and Results (subfield breakdown)"}],"minor_comments":[{"comment":"Provide explicit definitions and detection criteria for open-code status, open-data status, and open-access status, including any thresholds or external databases used.","section":"Methods"},{"comment":"Report the exact regression specification (functional form, interaction terms, variance inflation factors) and the robustness checks performed against the listed controls.","section":"Methods"},{"comment":"Clarify whether the percentage advantages are derived from exponentiated coefficients or marginal effects and how they account for the citation-count distribution.","section":"Results"}],"recommendation":"major_revision","confidential_remarks":null},"author_rebuttal":{"model":"grok-4.3","summary":"We thank the referee for their detailed and constructive report. We address each major comment below and outline planned revisions to strengthen the manuscript.","responses":[{"response":"We agree this is a substantive limitation: no direct measure of intrinsic quality, novelty, or rigor is available in the ADS-derived dataset, and residual confounding remains possible. Number of grants, author count, and paper length provide partial proxies (as these correlate with perceived quality and resources), and the combination of multivariate regression, partial correlations, and non-parametric tests offers some triangulation. However, these do not fully substitute for a quality proxy. In revision we will (1) add an explicit limitations subsection discussing this issue and the direction of potential bias, and (2) report additional sensitivity checks (e.g., subfield-stratified models and robustness to alternative specifications). We will not claim full causal isolation but will qualify the language accordingly.","revision_made":"yes","referee_comment":"[Abstract and Methods (regression specification)] The multivariate least-squares regression (described in the abstract and methods) controls for grants, authors, length, date, subfield, code size, language, and repo size but includes no proxy for intrinsic paper quality, novelty, or rigor. This omission risks confounding the reported +32%/+26%/+16% coefficients, as openness decisions and citation rates may both be driven by unmeasured quality; the central claim that controls isolate the contribution of openness therefore requires additional justification or sensitivity tests."},{"response":"Keyword-based subfield assignment follows standard practice in ADS analyses and is the only scalable approach for 53k papers. The manuscript already flags that Solar System and ISM rates are depressed by external platforms. The regression treats subfield as a categorical control, and the open-data coefficient remains positive in every subfield. To address the referee's concern we will expand the results and discussion sections with (a) more detail on known subfield data practices and (b) explicit caveats about possible residual variation in citation norms. We will also verify that the reported advantage holds after additional interaction terms between openness and subfield.","revision_made":"partial","referee_comment":"[Abstract and Results (subfield breakdown)] Subfield classification is performed via keywords, yet the abstract notes that low sharing rates in Solar System and ISM partly reflect platforms not captured by the study. This raises the possibility that subfield-specific citation norms or data practices are incompletely controlled, which could affect the claim that the open-data advantage is present (and strongest) in all six sub-fields."}],"tokens_in":1596,"tokens_out":546,"duration_ms":18758,"standing_objections":[]},"desk_editor":{"model":"grok-4.3","letter":"The core result is a set of regression coefficients showing citation lifts of roughly 32% for open data, 26% for open access, and 16% for open code in 53k recent astrophysics papers, with the open-data effect holding across all six subfields they split on.\n\nThey pull a large ADS sample, code the openness variables, and run multivariate least-squares plus partial correlations and non-parametrics while controlling for grants, author count, length, date, subfield, code size, language, and repo size. The subfield stratification is the clearest addition over prior citation-advantage work; it shows the biggest open-data premiums in Galaxies+Cosmology and ISM, which matches what one would expect from their data-sharing norms.\n\nThe soft spot is the lack of any proxy for intrinsic paper quality or visibility. Openness decisions and citation rates can both track unmeasured factors like novelty, team reputation, or subfield-specific practices that the keyword split does not fully capture. The reported p-values are small, but that does not rule out selection bias in the coefficients. The controls are sensible as far as they go, yet the central percentages still rest on the assumption that those variables absorb the main confounds.\n\nThis is the sort of empirical note that policy people and open-science advocates in astrophysics would want to see. A methods-focused reader or someone writing a review on citation incentives would get value from the sample size and the subfield tables. It is coherent on its own terms and deserves referee time even if the interpretation needs tightening.","headline":"This paper gives astro-specific citation numbers for open data/code/access after basic controls, but omitted quality measures leave the causal story open to doubt.","tokens_in":2438,"tokens_out":391,"would_cite":false,"duration_ms":14985,"reading_group":"maybe","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"grok-4.3","headline":"Astrophysics papers with open data receive 32 percent more citations after statistical controls for other factors.","keywords":["open science","citations","astrophysics","open data","open access","open code","regression"],"falsifier":"A new analysis of a comparable paper sample that adds direct measures of intrinsic paper quality and still finds no citation difference for open versus closed papers would falsify the reported advantages.","tokens_in":2742,"feed_emoji":"📈","tokens_out":440,"duration_ms":19895,"temperature":0.7,"pith_summary":"The paper measures citation effects of openness in a sample of over 53,000 astrophysics papers from 2021 to 2025. It tracks open data, open code, and open access status along with variables such as grants received, author count, and paper length. Multivariate regression, partial correlations, and non-parametric tests are used to separate the role of openness from those other influences. The analysis shows positive citation associations for all three openness types, strongest for open data and present across all six sub-fields examined.","feed_headline":"Open data tied to 32% citation boost in astrophysics","feed_subtitle":"Regression on 53k papers shows open access adds 26% and open code adds 16% after controls for grants and length.","key_machinery":"Multivariate least-squares regression together with partial correlations and non-parametric tests that isolate the contribution of each openness variable from the other measured quantities.","core_discovery":"After controlling for grants, code size, data repository size, programming language, number of authors, paper length, and publication date, open data is associated with a 32 percent citation increase, open access with 26 percent, and open code with 16 percent; the open-data advantage appears in every sub-field and is largest in Galaxies+Cosmology and ISM.","pith_inferences":[],"forward_implications":[],"fun_headline_variants":["Open data tied to 32% citation increase in astrophysics","Open access tied to 26% citation increase in astrophysics","Open code tied to 16% citation increase after controls","Open data advantage seen across all astrophysics subfields"],"cache_read_input_tokens":2112,"weakest_assumption_plain":"The regression and partial correlations successfully remove the effects of unmeasured paper quality, subfield citation norms, and selection biases that could link openness to higher citations.","fun_headline_variants_meta":{"raw":{"variants":["Open data tied to 32% citation increase in astrophysics","Open access tied to 26% citation increase in astrophysics","Open code tied to 16% citation increase after controls","Open data advantage seen across all astrophysics subfields"]},"model":"grok-4.3","cost_usd":0.007385,"raw_usage":{"total_tokens":3449,"prompt_tokens":775,"num_sources_used":0,"completion_tokens":66,"cost_in_usd_ticks":73849500,"prompt_tokens_details":{"text_tokens":775,"audio_tokens":0,"image_tokens":0,"cached_tokens":256},"completion_tokens_details":{"audio_tokens":0,"reasoning_tokens":2608,"accepted_prediction_tokens":0,"rejected_prediction_tokens":0}},"tokens_in":775,"tokens_out":66,"duration_ms":18093,"temperature":1.0,"reasoning_tokens":2608,"cache_read_input_tokens":256,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-07-02T06:00:47.455342+00:00","model_set":{"reader":"grok-4.3"},"falsifier":"A new analysis of a comparable paper sample that adds direct measures of intrinsic paper quality and still finds no citation difference for open versus closed papers would falsify the reported advantages.","supporting_citations":[],"review_version":1}