{"id":"W2964188068","doi":"10.1111/cars.12251","title":"How the State Shaped the Nonprofit Sector: Public Funding in British Columbia","year":2019,"lang":"en","type":"article","venue":"Canadian Review of Sociology/Revue canadienne de sociologie","topic":"Nonprofit Sector and Volunteering","field":"Social Sciences","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Thriving; Nonprofit sector; Public administration; State (computer science); Politics; Government (linguistics); Public sector; Public funding; Political science; Scope (computer science); Private sector; Power (physics); Economic growth; Business; Public relations; Economics; Sociology; Law; Social science","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006183321,0.0002006268,0.000681687,0.00008467919,0.0009069456,0.0002002641,0.001463357,0.000377224,0.000779668],"category_scores_gemma":[0.002828591,0.0002100416,0.0002820447,0.0005543024,0.001809419,0.0002088157,0.00008107237,0.000916398,0.000027116],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003107198,"about_ca_system_score_gemma":0.0031098,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8205582,"about_ca_topic_score_gemma":0.9980543,"domain_scores_codex":[0.9954839,0.001260364,0.0004743391,0.0004912157,0.000116908,0.002173249],"domain_scores_gemma":[0.9973624,0.0009234676,0.0003037201,0.0005413503,0.0002393914,0.000629716],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000001147527,0.000007458693,0.9666259,0.0008973504,0.00007630129,0.00004690741,0.01786694,0.000001715295,0.00007687727,0.004551403,0.005186686,0.004661291],"study_design_scores_gemma":[0.0004064827,0.0001016912,0.7481688,0.002122254,0.00006247677,0.00003019035,0.09111229,0.00004498668,9.641628e-7,0.00955676,0.1477301,0.0006630628],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.94829,0.02483416,0.000003758474,0.02233562,0.0008667249,0.001431292,0.0001455566,0.00004224348,0.002050666],"genre_scores_gemma":[0.9810564,0.01135482,0.00002415976,0.004668581,0.0002804475,0.0001740237,0.00003078265,0.00003337195,0.002377393],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2184571,"threshold_uncertainty_score":0.8565248,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06690751445030182,"score_gpt":0.2722867856315857,"score_spread":0.2053792711812839,"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."}}