{"id":"W1978350258","doi":"10.1504/ijtcs.2008.019177","title":"Self-efficacy and KM course weighting selection: can students optimise their grades?","year":2008,"lang":"en","type":"article","venue":"International Journal of Teaching and Case Studies","topic":"Management and Marketing Education","field":"Business, Management and Accounting","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lakehead University; McMaster University","funders":"","keywords":"Weighting; Course (navigation); A priori and a posteriori; Computer science; Selection (genetic algorithm); Mathematics education; Psychology; Medical education; Machine learning; Engineering; Medicine","routes":{"ca_aff":true,"ca_fund":false,"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":[],"consensus_categories":[],"category_scores_codex":[0.0009812108,0.0001334839,0.0001723447,0.0002691925,0.0005754977,0.0002194244,0.0001461843,0.00002169419,0.000007846409],"category_scores_gemma":[0.0002951548,0.0001038738,0.00004498454,0.00007317158,0.00005344069,0.0004955613,0.0001786729,0.0002438669,0.000001278268],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004003827,"about_ca_system_score_gemma":0.00001509566,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008028709,"about_ca_topic_score_gemma":0.00001983214,"domain_scores_codex":[0.9991136,0.00003872983,0.0002854498,0.0001348046,0.0003108508,0.0001166248],"domain_scores_gemma":[0.9990823,0.0001358356,0.0003790195,0.00004441895,0.0003407778,0.00001769508],"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":[0.0005683786,0.003347568,0.724128,0.0007074089,0.008692058,0.006931518,0.03049328,0.0002483366,0.000150323,0.01027584,0.09168459,0.1227727],"study_design_scores_gemma":[0.01751281,0.0005539082,0.3124384,0.002428036,0.002903046,0.05931467,0.09051592,0.01066521,0.00009123747,0.004938915,0.49601,0.002627809],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9932401,0.0008937196,0.00003738047,0.002934163,0.0008817326,0.00007583304,8.383143e-7,0.00003830615,0.00189791],"genre_scores_gemma":[0.9958702,0.0006995763,0.0008174149,0.0005089298,0.00175089,0.000002712074,0.000001920515,0.00001070047,0.0003375962],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4116896,"threshold_uncertainty_score":0.4426321,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02626441800672392,"score_gpt":0.311452796762306,"score_spread":0.2851883787555821,"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."}}