{"id":"W2897306483","doi":"10.1287/orsc.2018.1217","title":"Future-Time Framing: The Effect of Language on Corporate Future Orientation","year":2018,"lang":"en","type":"article","venue":"Organization Science","topic":"International Business and FDI","field":"Business, Management and Accounting","cited_by":111,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Universiteit Antwerpen; Universiteit van Tilburg; Singapore Management University; Universiteit Gent; National University of Singapore; Stockholms Universitet; York University; Harvard Business School","keywords":"Framing (construction); Categorization; Sociology; Corporate social responsibility; Political science; Public relations; Business; Linguistics; History","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004589518,0.00007716615,0.00007013294,0.000151567,0.0003327191,0.0002063342,0.0003193251,0.00002602588,0.0004931698],"category_scores_gemma":[0.0002554926,0.00004699267,0.00001305837,0.002371915,0.0002103434,0.0007310422,0.0000694919,0.00004146588,0.0006664175],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001972037,"about_ca_system_score_gemma":0.00002352685,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001927122,"about_ca_topic_score_gemma":0.000002310457,"domain_scores_codex":[0.9991868,0.000007664616,0.0001168457,0.0001705543,0.0004143735,0.0001038327],"domain_scores_gemma":[0.9988065,0.00002181757,0.0002567112,0.0001607442,0.0007485898,0.000005684375],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0001825312,0.00011734,0.1370879,0.0001518264,0.000027027,0.000007513233,0.002276782,0.0002481169,0.4142584,0.4042057,0.01423408,0.02720274],"study_design_scores_gemma":[0.00145244,0.0002451199,0.6389238,0.0001003386,0.00008135406,0.000007387711,0.001416305,0.01421336,0.3069461,0.001118516,0.03490088,0.0005944284],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9907801,0.000006204439,0.001026766,0.00149953,0.001330869,0.0001334537,0.000001363804,0.00004418546,0.005177572],"genre_scores_gemma":[0.9954227,8.124553e-7,0.00003892946,0.001022427,0.003230562,0.000001486462,0.00002142744,0.000009946698,0.0002517446],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5018359,"threshold_uncertainty_score":0.8565671,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005542369239064571,"score_gpt":0.224700973469019,"score_spread":0.2191586042299544,"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."}}