{"id":"W2751883854","doi":"10.1177/1052562917730381","title":"Population Ecology (Organizational Ecology): An Experiential Exercise Demonstrating How Organizations in an Industry Are Born, Change, and Die","year":2017,"lang":"en","type":"article","venue":"Organizational Behavior Teaching Review","topic":"Complex Systems and Decision Making","field":"Decision Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Wilfrid Laurier University","funders":"","keywords":"Organizational ecology; Ecology; Experiential learning; Population; Population ecology; Macro; Organizational behavior; Organizational learning; Psychology; Affect (linguistics); Sociology; Knowledge management; Social psychology; Mathematics education; Social science; Computer science; Biology","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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow","sts","scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002451735,0.0003605985,0.0008180723,0.0004287784,0.002335202,0.001492402,0.00116659,0.0004100402,0.00116286],"category_scores_gemma":[0.01077044,0.0003296135,0.00005548909,0.0007638923,0.0001418707,0.00264526,0.000501108,0.0006409303,0.0000389713],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001360751,"about_ca_system_score_gemma":0.0001605847,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001032892,"about_ca_topic_score_gemma":0.001522409,"domain_scores_codex":[0.9954851,0.00073954,0.001192866,0.001087509,0.001114872,0.0003800925],"domain_scores_gemma":[0.9956691,0.0003163146,0.001370015,0.001184409,0.001153885,0.0003063236],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000003033602,0.0002075572,0.98717,0.00003147083,0.000003169778,0.00003169948,0.0003807436,0.00001222609,0.0001293669,0.003115252,0.0002082131,0.008707242],"study_design_scores_gemma":[0.0003442959,0.00004782526,0.9959205,0.0005649715,0.00006174334,0.00009892513,0.0005396663,0.0005329515,0.000009887224,0.001118002,0.0003995115,0.0003617398],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9938278,0.002056275,0.0006452075,0.001521649,0.000650684,0.001076979,0.00006765282,0.00009574329,0.00005800828],"genre_scores_gemma":[0.9960146,0.0002106144,0.002297485,0.0004540105,0.000390493,0.0001045877,0.0002484583,0.00007089906,0.0002088621],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008750455,"threshold_uncertainty_score":0.9999156,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1572010526658797,"score_gpt":0.4221731777710299,"score_spread":0.2649721251051502,"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."}}