{"id":"W6968142401","doi":"10.5281/zenodo.15331149","title":"Exploring Intersections and Integrations: Advancing Equity in Educational HR Analytics","year":2025,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Water Resources and Sustainability","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University Canada West; Simon Fraser University","funders":"","keywords":"Learning analytics; Thematic analysis; Analytics; Transformative learning; Leverage (statistics); Equity (law); Educational equity; Realm","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00060879,0.00006519008,0.00006590991,0.0002047377,0.001111939,0.0002947403,0.0003309633,0.00001669298,0.003288547],"category_scores_gemma":[0.0006908565,0.00006717851,0.00001679024,0.0007737774,0.0002539881,0.0004410613,0.001543673,0.000156735,0.0002602736],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007216374,"about_ca_system_score_gemma":0.000004609017,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002179353,"about_ca_topic_score_gemma":0.00002349203,"domain_scores_codex":[0.9991216,0.00008248298,0.0001573548,0.0002637095,0.0001563036,0.0002185275],"domain_scores_gemma":[0.9996138,0.00002114749,0.00002624219,0.0001945324,0.00006802358,0.00007624053],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001801535,0.001048179,0.01650487,0.0002965967,0.0000664898,0.0000126909,0.02567764,0.008162033,0.0154065,0.04802378,0.04649547,0.8381256],"study_design_scores_gemma":[0.0004103662,0.00009546304,0.1615548,0.00006128549,0.00001204816,0.0000202279,0.007267884,0.004331473,0.000486914,0.006268132,0.8192894,0.0002020061],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8609143,0.00001856121,0.002723014,0.001935196,0.00006711436,0.0002524134,0.00001147309,0.0001103035,0.1339676],"genre_scores_gemma":[0.9989566,0.00002892894,0.0002379568,0.0000598558,0.00001721357,1.272221e-7,0.00004808217,0.00008584977,0.0005654154],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8379236,"threshold_uncertainty_score":0.9976226,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07086874586979228,"score_gpt":0.2949430556491482,"score_spread":0.2240743097793559,"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."}}