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1{2 "validation_errors": [],3 "repairs": [4  "dropped tables.figures"5 ],6 "doc_id": "jof_12619",7 "ontology_version": "1.0",8 "prompt_version": "1.0",9 "model": "claude-opus-5-5",10 "effort": "medium",11 "annotated_at": "2026-11-03T23:26:10+00:00",12 "source_sha256": "034e718ad966ca13da4b9fbd3f0140a545b75993676a7dd9083c26ee5daff048",13 "parser": "pymupdf-jof-heuristics-1.3",14 "payload_chars": 15389,15 "usage": {16  "input": 5071,17  "output": 2012,18  "cache_read": 3968,19  "cache_write": 020 },21 "cost_usd": 0.061318,22 "request_id": "req_011Cggpuj5yY8TLUtu2MwPiY",23 "annotation": {24  "genre": "empirical",25  "subfield": "behavioral_finance",26  "subfield_secondary": "market_microstructure (experimental asset markets)",27  "primary_method": "survey_or_experiment",28  "identification": {29   "estimand": "Association between trader cognitive skills (Raven fluid intelligence, CRT cognitive reflection, ToM) and per-period trading earnings, and the interaction of CRT with ToM, in a controlled experimental asset market",30   "strategy": "Laboratory asset markets in which private signals are randomized across traders so that skill is not confounded with information. Earnings are regressed on skill scores in linear panel regressions with subject random effects, controlling for individual and market characteristics.",31   "key_assumptions": [32    "Random allocation of private signals makes high-skill traders no better informed on average",33    "Raven, CRT and ToM scores validly measure the targeted skills",34    "The individual controls (financial literacy, personality, risk attitudes) capture relevant confounding traits"35   ],36   "threats_addressed": [37    "Confounding of skill with insider/private information, addressed by randomizing signals",38    "Omitted individual characteristics, addressed by a large set of individual-level controls"39   ],40   "causal_language_strength": 3,41   "uses_causal_claim": true42  },43  "abstract_moves": {44   "question": {45    "present": true,46    "sentence_index": 047   },48   "gap": {49    "present": false,50    "sentence_index": -151   },52   "method": {53    "present": true,54    "sentence_index": 055   },56   "results": {57    "present": true,58    "sentence_index": 459   },60   "magnitude": {61    "present": false,62    "sentence_index": -163   },64   "contribution": {65    "present": false,66    "sentence_index": -167   }68  },69  "introduction": {70   "moves": [71    {72     "paragraph_index": 0,73     "move": "motivation"74    },75    {76     "paragraph_index": 0,77     "move": "related_literature_positioning"78    },79    {80     "paragraph_index": 1,81     "move": "motivation"82    },83    {84     "paragraph_index": 2,85     "move": "literature_gap"86    },87    {88     "paragraph_index": 2,89     "move": "preview_of_approach"90    },91    {92     "paragraph_index": 2,93     "move": "identification_defense"94    },95    {96     "paragraph_index": 3,97     "move": "mechanism_or_theory_sketch"98    },99    {100     "paragraph_index": 4,101     "move": "mechanism_or_theory_sketch"102    },103    {104     "paragraph_index": 5,105     "move": "preview_of_results"106    },107    {108     "paragraph_index": 6,109     "move": "roadmap"110    }111   ],112   "contribution_paragraph_index": 2,113   "contribution_types": [114    "new_evidence"115   ],116   "roadmap_present": true,117   "literature_positioning_location": "both"118  },119  "section_roles": [120   {121    "idx": 0,122    "role": "introduction"123   },124   {125    "idx": 1,126    "role": "literature"127   },128   {129    "idx": 2,130    "role": "empirical_design"131   },132   {133    "idx": 3,134    "role": "theory"135   },136   {137    "idx": 4,138    "role": "results"139   },140   {141    "idx": 5,142    "role": "conclusion"143   }144  ],145  "results_presentation": {146   "economic_significance_discussed": false,147   "statistical_significance_language": "unclear",148   "magnitude_statements_in_intro": false149  },150  "robustness": {151   "location": "none_seen",152   "types": []153  },154  "tables": {155   "convention_notes": "Table notes are descriptive. They state the model (linear panel regressions with subject random effects) and list the controls, and they open with a one-sentence statement of the result. Robust standard errors clustered at the session level are reported in parentheses. A design summary table describes the treatments.",156   "units_stated_in_notes": true,157   "standard_errors_description": "clustered"158  },159  "figures": {160   "uncertainty_shown": "no"161  },162  "conclusion": {163   "bounded_claims": true,164   "policy_implications": false,165   "open_questions": false,166   "restates_contribution": true167  },168  "hedging": {169   "level": "moderate",170   "typical_devices": [171    "conjecture framing (we expect, we conjecture)",172    "scoping results to 'our market environment'",173    "noting that other market settings may differ",174    "citing an independent replication as support"175   ]176  },177  "claim_evidence": [178   {179    "claim": "Fluid intelligence, cognitive reflection and ToM scores are positively related to trader earnings",180    "evidence_type": "table",181    "evidence_ref": "Table II; Figure 1"182   },183   {184    "claim": "Cognitive reflection and ToM skills matter more when a trader's private hint is imprecise",185    "evidence_type": "table",186    "evidence_ref": "Table III"187   },188   {189    "claim": "Skills matter more when skill heterogeneity among traders in a market is high",190    "evidence_type": "table",191    "evidence_ref": "Table IV"192   },193   {194    "claim": "High-skill traders submit a larger share of potentially profitable orders",195    "evidence_type": "figure",196    "evidence_ref": "Figure 2"197   },198   {199    "claim": "ToM is key to assessing the informational content of trades",200    "evidence_type": "prior_literature",201    "evidence_ref": "Bruguier, Quartz, and Bossaerts (2010)"202   },203   {204    "claim": "The main findings are confirmed in a later independent study",205    "evidence_type": "prior_literature",206    "evidence_ref": "Hefti, Heinke, and Schneider (2016)"207   }208  ],209  "structural_summary": "The introduction motivates the question by challenging the rationality assumption with evidence of cognitive heterogeneity. It notes that prior work studies trader skills in isolation and proposes a unified laboratory market with randomized private signals. It then sketches the mechanisms for each skill and for the CRT–ToM complementarity. A separate literature section is organized by skill, followed by the design section. A dedicated conjectures section derives five hypotheses, which the results section tests one by one using random-effects panel regressions. The conclusion restates the findings, bounds their scope to information-driven markets, and cites an independent replication.",210  "writing_profile_keywords": [211   "laboratory experiment",212   "conjecture-driven",213   "cognitive skills",214   "theory of mind",215   "random effects panel",216   "behavioral finance"217  ],218  "confidence": 0.8,219  "notes": "Robustness checks are not visible in the excerpts; an Internet Appendix exists, but its contents are unknown. The conjectures section was classified as theory, although it is informal reasoning rather than a model. Significance reporting conventions (stars, t-statistics) are not visible."220 },221 "deterministic_checks": {222  "roadmap_regex": true,223  "contribution_regex_in_intro": false,224  "magnitude_in_abstract": false,225  "abstract_n_sentences": 5,226  "intro_n_paragraphs": 7,227  "intro_n_words": 775,228  "causal_words_in_abstract": 0,229  "hedge_words_in_abstract": 0,230  "stars_mentioned": false,231  "clustered_se_mentioned": true,232  "internet_appendix_mentioned": false,233  "section_roles_rule": {234   "0": "introduction",235   "1": "literature",236   "2": "other",237   "3": "other",238   "4": "results",239   "5": "conclusion"240  }241 },242 "schema_valid": true,243 "code_version": "87c73cd-dirty"244}
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