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1{2 "validation_errors": [],3 "doc_id": "jof_12768",4 "ontology_version": "1.0",5 "prompt_version": "1.0",6 "model": "claude-opus-5-5",7 "effort": "medium",8 "annotated_at": "2026-11-03T23:08:30+00:00",9 "source_sha256": "1d24c8f974b29cb6f2b1d51ef4a7478f16e44a772cee214e4bc82339b053cc3b",10 "parser": "pymupdf-jof-heuristics-1.3",11 "payload_chars": 21201,12 "usage": {13  "input": 6629,14  "output": 1881,15  "cache_read": 3968,16  "cache_write": 017 },18 "cost_usd": 0.06493,19 "request_id": "req_011CggqSHKnsFwpsf8BVeY35",20 "annotation": {21  "genre": "empirical",22  "subfield": "asset_pricing",23  "subfield_secondary": "market_microstructure; experimental finance",24  "primary_method": "survey_or_experiment",25  "identification": {26   "estimand": "Treatment effects of exogenous information-network structure (none, complete, circle, core-periphery) on signal acquisition, volume, liquidity, information aggregation, price informativeness and profits",27   "strategy": "Between-session laboratory treatments that randomly vary the information network, analyzed with ordered probit and OLS regressions on treatment dummies, using standard errors clustered at subject level",28   "key_assumptions": [29    "Network structure is exogenous and assigned by design",30    "Information sharing is automatic, perfect and nonstrategic",31    "Subjects in different sessions are comparable"32   ],33   "threats_addressed": [34    "Endogenous network formation (removed by imposing exogenous networks)",35    "Within-subject dependence (handled by clustering)"36   ],37   "causal_language_strength": 4,38   "uses_causal_claim": true39  },40  "abstract_moves": {41   "question": {42    "present": true,43    "sentence_index": 044   },45   "gap": {46    "present": false,47    "sentence_index": -148   },49   "method": {50    "present": true,51    "sentence_index": 052   },53   "results": {54    "present": true,55    "sentence_index": 156   },57   "magnitude": {58    "present": false,59    "sentence_index": -160   },61   "contribution": {62    "present": false,63    "sentence_index": -164   }65  },66  "introduction": {67   "moves": [68    {69     "paragraph_index": 0,70     "move": "motivation"71    },72    {73     "paragraph_index": 0,74     "move": "literature_gap"75    },76    {77     "paragraph_index": 1,78     "move": "preview_of_approach"79    },80    {81     "paragraph_index": 2,82     "move": "mechanism_or_theory_sketch"83    },84    {85     "paragraph_index": 2,86     "move": "preview_of_results"87    },88    {89     "paragraph_index": 3,90     "move": "preview_of_results"91    },92    {93     "paragraph_index": 4,94     "move": "preview_of_results"95    },96    {97     "paragraph_index": 4,98     "move": "tension_or_puzzle"99    },100    {101     "paragraph_index": 4,102     "move": "related_literature_positioning"103    }104   ],105   "contribution_paragraph_index": 1,106   "contribution_types": [107    "new_evidence"108   ],109   "roadmap_present": false,110   "literature_positioning_location": "inside_introduction"111  },112  "section_roles": [113   {114    "idx": 0,115    "role": "introduction"116   },117   {118    "idx": 1,119    "role": "empirical_design"120   },121   {122    "idx": 2,123    "role": "results"124   },125   {126    "idx": 3,127    "role": "other"128   },129   {130    "idx": 4,131    "role": "conclusion"132   }133  ],134  "results_presentation": {135   "economic_significance_discussed": true,136   "statistical_significance_language": "stars_and_tstats",137   "magnitude_statements_in_intro": true138  },139  "robustness": {140   "location": "none_seen",141   "types": []142  },143  "tables": {144   "convention_notes": "Tables pair period-average descriptive statistics, reported with standard deviations, with regression tables. The regressions use treatment dummies against a nonnetworked baseline and include a period control. Standard errors are clustered by subject and reported in parentheses, with stars marking significance. Notes define each dependent variable precisely.",145   "units_stated_in_notes": true,146   "standard_errors_description": "clustered"147  },148  "figures": {149   "uncertainty_shown": "mixed"150  },151  "conclusion": {152   "bounded_claims": true,153   "policy_implications": false,154   "open_questions": true,155   "restates_contribution": true156  },157  "hedging": {158   "level": "moderate",159   "typical_devices": [160    "on average",161    "typically",162    "explicit design simplifications acknowledged",163    "might not be as strong"164   ]165  },166  "claim_evidence": [167   {168    "claim": "Signal acquisition probability and quantity fall as a trader's number of network connections rises.",169    "evidence_type": "table",170    "evidence_ref": "Tables II, III, IV; Figure 2"171   },172   {173    "claim": "Information sharing raises trading volume and improves liquidity.",174    "evidence_type": "table",175    "evidence_ref": "Table V"176   },177   {178    "claim": "Prices aggregate more of the available information with sharing, and aggregation rises with network density.",179    "evidence_type": "table",180    "evidence_ref": "Table VI; Figures 3-4"181   },182   {183    "claim": "Price informativeness about the true state does not improve with social communication.",184    "evidence_type": "table",185    "evidence_ref": "Table IX; Figures 5-6"186   },187   {188    "claim": "Net earnings are higher under sharing because of savings on signal costs.",189    "evidence_type": "table",190    "evidence_ref": "Tables X, XIII"191   },192   {193    "claim": "Prices support semi-strong but not strong-form efficiency.",194    "evidence_type": "prior_literature",195    "evidence_ref": "Discussion; Page and Siemroth (2018)"196   }197  ],198  "structural_summary": "The introduction opens with the rational-expectations literature on costly information and flags the absence of social networks as an open question. It then describes the exogenous-network setup and frames a trade-off between richer information sets and free-riding. Results are previewed sequentially: crowding out of acquisition, then liquidity, aggregation, informativeness and earnings, with a counter-to-conventional-wisdom point on forecasting quality. A design section presents the lab procedures and the four network treatments. A long results section is organized by outcome, using regressions on treatment dummies. A discussion section links the findings to market efficiency, and a short conclusion restates the findings and lists extensions.",199  "writing_profile_keywords": [200   "laboratory experiment",201   "information networks",202   "free-riding",203   "treatment comparisons",204   "price efficiency",205   "outcome-by-outcome results"206  ],207  "confidence": 0.82,208  "notes": "Experimental asset-market paper with no equations shown. No explicit robustness section is visible in the excerpts. An Internet Appendix exists, but its contents are not shown."209 },210 "deterministic_checks": {211  "roadmap_regex": false,212  "contribution_regex_in_intro": false,213  "magnitude_in_abstract": false,214  "abstract_n_sentences": 4,215  "intro_n_paragraphs": 5,216  "intro_n_words": 792,217  "causal_words_in_abstract": 0,218  "hedge_words_in_abstract": 0,219  "stars_mentioned": false,220  "clustered_se_mentioned": true,221  "internet_appendix_mentioned": false,222  "section_roles_rule": {223   "0": "introduction",224   "1": "other",225   "2": "results",226   "3": "conclusion",227   "4": "conclusion"228  }229 },230 "schema_valid": true,231 "code_version": "87c73cd-dirty"232}
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