{"id":"W3034995498","doi":"10.33137/ijournal.v5i2.34469","title":"Powering digital communities: How Public Libraries Can Foster Digital Inclusion and Digital Literacy in Ontario","year":2020,"lang":"en","type":"article","venue":"The iJournal Student Journal of the Faculty of Information","topic":"Library Science and Administration","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Digitization; Digital literacy; Literacy; Digital divide; Public relations; Inclusion (mineral); Population; Face (sociological concept); Digital inclusion; Resource (disambiguation); Business; Political science; Internet privacy; Sociology; World Wide Web; Engineering; Computer science; Information and Communications Technology; The Internet; Pedagogy; Social science; Telecommunications","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.003991848,0.0002857753,0.0002437315,0.002078334,0.03074725,0.009261728,0.002014121,0.001449172,0.01662582],"category_scores_gemma":[0.01253373,0.000377977,0.0003885051,0.002719257,0.006757069,0.006681344,0.01209007,0.00115249,0.001381329],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.06742795,"about_ca_system_score_gemma":0.1635969,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.819384,"about_ca_topic_score_gemma":0.9460703,"domain_scores_codex":[0.9964666,0.001213707,0.00009674234,0.0001915484,0.0009473281,0.001084044],"domain_scores_gemma":[0.9899738,0.001770752,0.0005643581,0.0005011288,0.002720166,0.004469823],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.0003317698,0.001021061,0.06442188,0.0008828433,0.00005608234,0.003978511,0.3903454,0.001462875,0.002771677,0.08810501,0.08002693,0.366596],"study_design_scores_gemma":[0.0001329731,0.0002336419,0.03388344,0.000536368,0.00008845705,0.0002785606,0.3439741,0.001317343,0.001169456,0.01901728,0.5992884,0.00008003906],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.510777,0.001348261,0.00436098,0.05067431,0.0002782823,0.0007695287,0.0001406402,0.000423991,0.431227],"genre_scores_gemma":[0.93507,0.0009762542,0.00316732,0.001740434,0.00005232396,0.0001586532,0.000064721,0.00007816587,0.0586922],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9907383,"threshold_uncertainty_score":0.4892263,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04011707571110006,"score_gpt":0.2790481054953459,"score_spread":0.2389310297842459,"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."}}