{"id":"W7127274999","doi":"10.5281/zenodo.17856819","title":"The Distribution of Power and Inclusiveness across Deep Time, Feinman et al. Science Advances - R Scripts and Dataset","year":2025,"lang":"en","type":"dataset","venue":"Open MIND","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Scripting language; Population; Bureaucracy; Power (physics); Distribution (mathematics); Field (mathematics); Proxy (statistics)","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003336734,0.001256066,0.000869928,0.003369201,0.0009853912,0.002847053,0.002889966,0.001314409,0.08039582],"category_scores_gemma":[0.02405764,0.0007610024,0.001407948,0.005219244,0.0007143258,0.001371138,0.002615619,0.002071725,0.06747527],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001353471,"about_ca_system_score_gemma":0.002450628,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02145103,"about_ca_topic_score_gemma":0.04849503,"domain_scores_codex":[0.9981992,0.0004137558,0.0002068054,0.0005897009,0.0003813009,0.0002093338],"domain_scores_gemma":[0.9921435,0.004171496,0.000552073,0.001657851,0.001175842,0.0002991725],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00003856203,0.00001944702,0.002437666,0.0004540769,0.00004658318,0.00003373115,0.0001031679,0.0005808995,0.00008141034,0.001863501,0.9897171,0.004623881],"study_design_scores_gemma":[0.0001869777,0.00002063733,0.01051771,0.0003187661,0.000049585,0.00009088031,0.0001885441,0.00118233,0.0003411565,0.006997797,0.9800536,0.00005194041],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00047849,0.0000554817,0.0008307105,0.0001851403,0.00004305781,0.00004486279,0.9958062,0.001081941,0.001474048],"genre_scores_gemma":[0.002861844,0.00007410206,0.0046723,0.0001490026,0.00002352762,0.000996143,0.9880913,0.0008036147,0.002328061],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.08039582,"threshold_uncertainty_score":0.2689508,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01460547571687165,"score_gpt":0.3739631452071126,"score_spread":0.3593576694902409,"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."}}