{"id":"W3185847439","doi":"10.69520/jipe.v3i1.92","title":"Back to Basics: Facilitating the Recognition of Micro-Credentials in Ontario PSE","year":2021,"lang":"en","type":"article","venue":"Journal of innovation in polytechnic education.","topic":"Education Systems and Policy","field":"Social Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer 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":[0.003088558,0.0000711906,0.000186001,0.0006912992,0.00009083191,0.00005616374,0.0002088396,0.00008198054,0.0008567992],"category_scores_gemma":[0.001987116,0.00006489064,0.00004374098,0.003103885,0.00005712123,0.0002895479,0.00001767999,0.0002668956,0.00002696251],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005985834,"about_ca_system_score_gemma":0.006143617,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.02563669,"about_ca_topic_score_gemma":0.06430935,"domain_scores_codex":[0.9977154,0.0002805833,0.001439773,0.0001024969,0.0003114576,0.0001503214],"domain_scores_gemma":[0.9968688,0.0002578141,0.0008946779,0.0001540537,0.001784591,0.00004006356],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00005161182,0.001190539,0.4306663,0.0001049154,0.00004870463,0.000002125741,0.3919874,0.0001052288,0.03767139,0.06017592,0.03827537,0.03972051],"study_design_scores_gemma":[0.0006905093,0.00009306461,0.3824697,0.001399666,0.00001904384,0.00004687554,0.3740238,7.974959e-7,0.01366551,0.01405308,0.2132284,0.0003096152],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9793777,0.0001688228,0.0002627904,0.01053451,0.001148703,0.0002310615,0.000003447701,0.00000278786,0.008270102],"genre_scores_gemma":[0.9877125,0.00003498828,0.007232188,0.001027827,0.0003664963,0.00002366914,0.000009470283,0.000006973037,0.003585873],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.174953,"threshold_uncertainty_score":0.9994906,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06118507800171025,"score_gpt":0.371672619308648,"score_spread":0.3104875413069378,"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."}}