{"id":"W4283835354","doi":"10.5465/ambpp.2022.12907abstract","title":"Do Big Words Make a Big Difference in Funding Outcomes in Equity Crowdfunding?","year":2022,"lang":"en","type":"article","venue":"Academy of Management Proceedings","topic":"FinTech, Crowdfunding, Digital Finance","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Wilfrid Laurier University","funders":"","keywords":"Equity crowdfunding; Equity (law); Venture capital; Business; Investment (military); Seed money; Big data; Work (physics); Marketing; Finance; Computer science; Political science","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00338327,0.0002437438,0.0003673897,0.001744277,0.0006048763,0.002603937,0.0003815944,0.000836233,0.005603802],"category_scores_gemma":[0.03578458,0.000180237,0.0003791443,0.001686757,0.001496501,0.00281557,0.001668051,0.001012175,0.0006553368],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005369119,"about_ca_system_score_gemma":0.0005673541,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004201733,"about_ca_topic_score_gemma":0.004910937,"domain_scores_codex":[0.9973581,0.001278879,0.000227117,0.000320832,0.00046051,0.0003546021],"domain_scores_gemma":[0.960821,0.02449402,0.0105251,0.001235586,0.0009641353,0.001960154],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005042623,0.0001952203,0.949981,0.0001048354,0.0001143662,0.0003571598,0.01426223,0.000208638,0.0007697675,0.001644808,0.001074733,0.03078298],"study_design_scores_gemma":[0.00002019989,0.0001094737,0.978506,0.0001391788,0.0000917726,0.0002380439,0.01299002,0.0009132928,0.0003570553,0.004450563,0.002146115,0.00003820623],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9964803,0.0002487785,0.0002791056,0.0006778187,0.00001968145,0.000006663336,0.0001864077,0.000004461325,0.002096787],"genre_scores_gemma":[0.9992899,0.00008331219,0.0001139726,0.00006675433,0.00002339497,0.000007439597,0.0001107487,0.000005178205,0.000299312],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005603802,"threshold_uncertainty_score":0.01874655,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05805924283614596,"score_gpt":0.2879527550865643,"score_spread":0.2298935122504184,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}