{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01116225,0.0001743848,0.0002567845,0.004035763,0.003552724,0.01548349,0.001359029,0.0007603329,0.00463262],"category_scores_gemma":[0.03369536,0.0003112912,0.0003155082,0.008167269,0.006300742,0.01419587,0.01059745,0.001616265,0.0005871469],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004634608,"about_ca_system_score_gemma":0.006845032,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009331931,"about_ca_topic_score_gemma":0.007668127,"domain_scores_codex":[0.9904101,0.004982675,0.0007219145,0.001142293,0.001624785,0.001118224],"domain_scores_gemma":[0.961518,0.01721483,0.007198497,0.006136496,0.004676367,0.003255831],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"qualitative","study_design_scores_codex":[0.00005057342,0.00009299169,0.1446197,0.0002543364,0.00003199804,0.0004562024,0.05786548,0.001845762,0.0005270625,0.6934906,0.01002829,0.09073699],"study_design_scores_gemma":[0.00001344611,0.00005405533,0.1026823,0.0009451538,0.00002035018,0.0007142388,0.1874754,0.004180055,0.0007668366,0.3129022,0.3901697,0.00007618489],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6216764,0.001949298,0.0725399,0.03075115,0.0001894817,0.0002728607,0.003156418,0.0004035401,0.2690609],"genre_scores_gemma":[0.9773745,0.000647526,0.01688202,0.0006910594,0.00004384757,0.0001731426,0.001064605,0.0001050375,0.003018354],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01548349,"threshold_uncertainty_score":0.05903232,"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."}}