{"id":"W4224315841","doi":"10.1177/08997640221085731","title":"Understanding National Nonprofit Data Environments","year":2022,"lang":"en","type":"article","venue":"Nonprofit and Voluntary Sector Quarterly","topic":"Nonprofit Sector and Volunteering","field":"Social Sciences","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; University of Calgary; Concordia University","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Openness to experience; Civil society; Autonomy; Politics; Globalization; Information overload; Data quality; Survey data collection; Business; Transparency (behavior); Quality (philosophy); Public relations; Political science; Public administration; Marketing; Law","routes":{"ca_aff":true,"ca_fund":true,"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":["metaepi_narrow","sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00156205,0.000257158,0.0002619382,0.0001943937,0.002484087,0.000155334,0.000877972,0.00009525227,0.002778379],"category_scores_gemma":[0.00002950088,0.0002977091,0.00006932554,0.0003674079,0.0002735447,0.0008657699,0.0002964667,0.000474916,0.00005242595],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008591173,"about_ca_system_score_gemma":0.0002455362,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005337341,"about_ca_topic_score_gemma":0.002852285,"domain_scores_codex":[0.9965664,0.0003261476,0.0003473723,0.0007763539,0.001323354,0.0006603449],"domain_scores_gemma":[0.9989341,0.0001748042,0.0001359601,0.0004775107,0.0000252319,0.000252412],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0005208436,0.000575935,0.7704918,0.0001413612,0.0005346245,0.0001428884,0.0770307,0.00005986387,0.00785002,0.0901148,0.04221304,0.01032414],"study_design_scores_gemma":[0.00474503,0.002275486,0.1533936,0.00007333687,0.0002288042,0.00009079674,0.1424166,0.008400913,0.0001040496,0.02804892,0.6572007,0.003021796],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9604989,0.0007580941,0.004093169,0.002026357,0.003002859,0.001264245,0.0009829913,0.0003353017,0.02703807],"genre_scores_gemma":[0.9954057,0.00002737065,0.0003329391,0.0004594322,0.0008271612,0.00007035903,0.0003300566,0.00004405933,0.002502977],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6170982,"threshold_uncertainty_score":0.9999475,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2359493383532323,"score_gpt":0.3252626823006121,"score_spread":0.08931334394737986,"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."}}