{"id":"W7103168221","doi":"10.17605/osf.io/x245z","title":"Daily goal pursuit, affect, regulatory focus, and culture","year":2024,"lang":"","type":"other","venue":"Open Science Framework","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Process (computing); Data collection; Set (abstract data type); Government (linguistics)","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":[0.007128843,0.0003973191,0.000486257,0.00198382,0.002904421,0.004914611,0.001207066,0.001030854,0.2242417],"category_scores_gemma":[0.01652804,0.0003795681,0.0004493701,0.002066601,0.001077669,0.001504515,0.001505559,0.001474271,0.04008669],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002963477,"about_ca_system_score_gemma":0.006833272,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01937556,"about_ca_topic_score_gemma":0.03938114,"domain_scores_codex":[0.9981624,0.0007448407,0.00008376512,0.0001047478,0.000737675,0.0001665559],"domain_scores_gemma":[0.9828432,0.008983291,0.0005867514,0.001732341,0.004154584,0.001699747],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.0004897097,0.001053763,0.01261523,0.0003758824,0.00001965705,0.00006062027,0.002391308,0.0003817447,0.001037041,0.05549977,0.6701152,0.2559601],"study_design_scores_gemma":[0.0002032545,0.0008417286,0.256795,0.00068772,0.00006340883,0.00008433553,0.01365017,0.002965564,0.007331218,0.04731996,0.669874,0.0001837096],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.02722605,0.0003791843,0.01376815,0.009924403,0.000735493,0.003300351,0.02790953,0.001101904,0.9156551],"genre_scores_gemma":[0.1440312,0.0008265664,0.03538173,0.00105012,0.0002790833,0.009175948,0.01458743,0.0008942212,0.7937737],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.2242417,"threshold_uncertainty_score":0.7501631,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01644953817429392,"score_gpt":0.3332055096926993,"score_spread":0.3167559715184054,"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."}}