{"id":"W3093726379","doi":"10.1017/s0714980820000367","title":"Mitigating the Challenges and Capitalizing on Opportunities: A Qualitative Investigation of the Public Library’s Response to an Aging Population","year":2020,"lang":"en","type":"article","venue":"Canadian Journal on Aging / La Revue canadienne du vieillissement","topic":"Library Science and Administration","field":"Social Sciences","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University; Hamilton Health Sciences","funders":"Social Sciences and Humanities Research Council of Canada; Alzheimer Society","keywords":"Qualitative research; Population; Population ageing; Political science; Sociology; Social science","routes":{"ca_aff":true,"ca_fund":true,"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":["sts"],"consensus_categories":[],"category_scores_codex":[0.002348362,0.0001243635,0.000132616,0.0001841404,0.001347507,0.0004629862,0.0003963672,0.00004277197,0.00003745356],"category_scores_gemma":[0.001099588,0.00009359285,0.00004559003,0.0003602141,0.0002261238,0.001122137,0.00001943365,0.0002185262,0.000001025121],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002095769,"about_ca_system_score_gemma":0.001182746,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.008639031,"about_ca_topic_score_gemma":0.1695865,"domain_scores_codex":[0.9972931,0.00159412,0.0003069181,0.0002238306,0.0001976163,0.000384449],"domain_scores_gemma":[0.9976628,0.0003810641,0.0002432982,0.0001517079,0.0000771252,0.001484027],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.00001341142,0.000004406215,0.004355595,0.00002129606,0.00001367961,0.00004370925,0.7924775,0.0001224287,0.0001355146,0.1962436,0.0005241217,0.006044733],"study_design_scores_gemma":[0.0002731304,0.0006125886,0.07916237,0.0009778186,0.00002111117,0.00003022664,0.8140428,0.0005016965,0.000198592,0.007059829,0.0967223,0.0003975321],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6756468,0.0001362434,0.000002807812,0.3231395,0.0001676878,0.0001730777,0.00001383675,0.000009718757,0.0007102879],"genre_scores_gemma":[0.9916573,0.00008695645,0.00008760651,0.007757157,0.0002761195,0.000008480763,0.000006268049,0.00001396104,0.0001061778],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3160104,"threshold_uncertainty_score":0.9999526,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09685224944602035,"score_gpt":0.2904707943544526,"score_spread":0.1936185449084323,"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."}}