1{2 "validation_errors": [],3 "doc_id": "jof_12530",4 "ontology_version": "1.0",5 "prompt_version": "1.0",6 "model": "claude-opus-5-5",7 "effort": "medium",8 "annotated_at": "2026-11-03T22:57:47+00:00",9 "source_sha256": "e7091a0d4b8826fccbb9cdfd5a1a80d2b93abaa741a5d2db98fb07459b1721af",10 "parser": "pymupdf-jof-heuristics-1.3",11 "payload_chars": 21303,12 "usage": {13 "input": 7476,14 "output": 2100,15 "cache_read": 3968,16 "cache_write": 017 },18 "cost_usd": 0.072698,19 "request_id": "req_011CggpcpmpCGxdqD1gXthDB",20 "annotation": {21 "genre": "survey_or_address",22 "subfield": "financial_econometrics",23 "subfield_secondary": "research methodology / meta-science of publication bias in asset pricing",24 "primary_method": "other",25 "identification": {26 "estimand": "not applicable (methodological address); the target quantity is the posterior probability that the null is true given a reported p-value and prior odds",27 "strategy": "not applicable; conceptual argument, a hypothetical ticker-symbol data-mining example, and Bayesian conversion of p-values using minimum Bayes factors",28 "key_assumptions": [29 "prior odds on the null vs. alternative can be specified from economic plausibility",30 "minimum Bayes factor bounds the evidence against the null"31 ],32 "threats_addressed": [33 "multiple testing",34 "p-hacking",35 "publication bias / file drawer effect",36 "low prior probability of true effects (rare incidence)"37 ],38 "causal_language_strength": 2,39 "uses_causal_claim": false40 },41 "abstract_moves": {42 "question": {43 "present": true,44 "sentence_index": 445 },46 "gap": {47 "present": true,48 "sentence_index": 349 },50 "method": {51 "present": true,52 "sentence_index": 553 },54 "results": {55 "present": true,56 "sentence_index": 157 },58 "magnitude": {59 "present": false,60 "sentence_index": -161 },62 "contribution": {63 "present": true,64 "sentence_index": 665 }66 },67 "introduction": {68 "moves": [69 {70 "paragraph_index": 0,71 "move": "motivation"72 },73 {74 "paragraph_index": 0,75 "move": "tension_or_puzzle"76 },77 {78 "paragraph_index": 1,79 "move": "mechanism_or_theory_sketch"80 },81 {82 "paragraph_index": 2,83 "move": "related_literature_positioning"84 },85 {86 "paragraph_index": 2,87 "move": "policy_or_broader_implication"88 },89 {90 "paragraph_index": 2,91 "move": "roadmap"92 }93 ],94 "contribution_paragraph_index": 2,95 "contribution_types": [96 "new_method",97 "synthesis"98 ],99 "roadmap_present": true,100 "literature_positioning_location": "both"101 },102 "section_roles": [103 {104 "idx": 0,105 "role": "introduction"106 },107 {108 "idx": 1,109 "role": "literature"110 },111 {112 "idx": 2,113 "role": "other"114 },115 {116 "idx": 3,117 "role": "other"118 },119 {120 "idx": 4,121 "role": "literature"122 },123 {124 "idx": 5,125 "role": "theory"126 },127 {128 "idx": 6,129 "role": "other"130 },131 {132 "idx": 7,133 "role": "theory"134 },135 {136 "idx": 8,137 "role": "theory"138 },139 {140 "idx": 9,141 "role": "theory"142 },143 {144 "idx": 10,145 "role": "theory"146 },147 {148 "idx": 11,149 "role": "robustness"150 },151 {152 "idx": 12,153 "role": "other"154 },155 {156 "idx": 13,157 "role": "conclusion"158 }159 ],160 "results_presentation": {161 "economic_significance_discussed": false,162 "statistical_significance_language": "mixed",163 "magnitude_statements_in_intro": true164 },165 "robustness": {166 "location": "none_seen",167 "types": []168 },169 "tables": {170 "convention_notes": "Tables are illustrative computation grids. Rows are significance levels or z-scores/p-values, and columns are prior odds labeled from 'long shot' to 'even odds'. Notes define the MBF and SD-MBF formulas. One table re-expresses p-values from published papers as Bayesianized p-values.",171 "units_stated_in_notes": true,172 "standard_errors_description": "none"173 },174 "figures": {175 "uncertainty_shown": "no"176 },177 "conclusion": {178 "bounded_claims": true,179 "policy_implications": true,180 "open_questions": true,181 "restates_contribution": false182 },183 "hedging": {184 "level": "moderate",185 "typical_devices": [186 "modal verbs (may, likely)",187 "framing as a conversation starter",188 "disclaimer that past research quality is high",189 "extended quotations from authorities (Hubble, Russell)"190 ]191 },192 "claim_evidence": [193 {194 "claim": "Searching many specifications can produce a seemingly significant factor from economically meaningless sorting such as ticker letters.",195 "evidence_type": "descriptive_statistic",196 "evidence_ref": "Introduction ticker example (t=3.23)"197 },198 {199 "claim": "Published factor t-statistics cluster just above conventional thresholds, consistent with publication bias.",200 "evidence_type": "figure",201 "evidence_ref": "Figure 1, Figure 6 (HLZ 2016)"202 },203 {204 "claim": "Economics and business publish supportive results at rates close to psychology and above the physical sciences.",205 "evidence_type": "figure",206 "evidence_ref": "Figure 2 (Fanelli 2010)"207 },208 {209 "claim": "The share of positive results has risen over time across sciences.",210 "evidence_type": "figure",211 "evidence_ref": "Figure 3 (Fanelli 2012)"212 },213 {214 "claim": "With low prior odds, even strict significance thresholds leave a high probability that the null is true.",215 "evidence_type": "table",216 "evidence_ref": "Table I, Table II"217 },218 {219 "claim": "The required t-statistic threshold depends on the prior plausibility of the hypothesis.",220 "evidence_type": "table",221 "evidence_ref": "Table III"222 },223 {224 "claim": "Reported p-values in published factor papers translate into varied Bayesianized p-values depending on prior odds.",225 "evidence_type": "table",226 "evidence_ref": "Table IV"227 }228 ],229 "structural_summary": "A presidential address that opens with a provocative hypothetical data-mining example and then frames the problem as an agency conflict between editors and authors. It brings in meta-scientific evidence from other fields and reviews what p-values mean and their limitations. It then discusses p-hacking and the low base rates of true effects, and makes the case for Bayesian reasoning. As a compromise, it proposes minimum Bayes factors and illustrates them with tables and applications to published papers. It notes limitations, closes with research-culture recommendations, and ends with a reflective, quotation-rich conclusion.",230 "writing_profile_keywords": [231 "presidential address",232 "first-person voice",233 "meta-science",234 "p-hacking",235 "Bayesian inference",236 "minimum Bayes factor",237 "publication bias",238 "normative recommendations",239 "illustrative examples"240 ],241 "confidence": 0.8,242 "notes": "This is not an empirical paper with identification. The figures are mostly reproduced from other studies. Section headings idx 2–4 are artifacts of figure panel labels; idx 4 continues the discussion of Fanelli's explanations. Robustness checks are not applicable, and Section VIII discusses limitations of the method rather than reporting robustness tests."243 },244 "deterministic_checks": {245 "roadmap_regex": false,246 "contribution_regex_in_intro": true,247 "magnitude_in_abstract": true,248 "abstract_n_sentences": 16,249 "intro_n_paragraphs": 3,250 "intro_n_words": 600,251 "causal_words_in_abstract": 0,252 "hedge_words_in_abstract": 3,253 "stars_mentioned": false,254 "clustered_se_mentioned": false,255 "internet_appendix_mentioned": false,256 "section_roles_rule": {257 "0": "introduction",258 "1": "results",259 "2": "other",260 "3": "other",261 "4": "other",262 "5": "other",263 "6": "other",264 "7": "other",265 "8": "other",266 "9": "other",267 "10": "other",268 "11": "robustness",269 "12": "other",270 "13": "conclusion"271 }272 },273 "schema_valid": true,274 "code_version": "feb6c9e-dirty"275}