{"id":"W4409439245","doi":"10.1017/psrm.2025.19","title":"Reassessing World Bank conditionality: beyond count measures","year":2025,"lang":"en","type":"article","venue":"Political Science Research and Methods","topic":"International Development and Aid","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Conditionality; Economics; Political science; Law; Politics","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":[0.01003209,0.0004835441,0.0005705147,0.007365345,0.001738038,0.003915395,0.0007043342,0.0006379483,0.007633814],"category_scores_gemma":[0.1220173,0.0002516806,0.000490426,0.01290029,0.003353895,0.007276405,0.003082769,0.002246067,0.0006723627],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00107485,"about_ca_system_score_gemma":0.0007837752,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005115881,"about_ca_topic_score_gemma":0.003574634,"domain_scores_codex":[0.992784,0.004030839,0.0005989777,0.001235326,0.001117167,0.0002336588],"domain_scores_gemma":[0.8597802,0.1013891,0.0177648,0.01321317,0.006258446,0.001594211],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004904312,0.0002800088,0.5221396,0.0007212768,0.0005787009,0.0002992955,0.01023516,0.009205932,0.002383618,0.2635875,0.02450694,0.1655716],"study_design_scores_gemma":[0.00007538313,0.000187477,0.3752886,0.000516873,0.0002299098,0.0002892486,0.006154149,0.08030825,0.003178087,0.4923844,0.04115519,0.0002324013],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8217619,0.001561197,0.127958,0.006296005,0.0002776044,0.0001634926,0.009123676,0.000562971,0.03229513],"genre_scores_gemma":[0.9866002,0.0001694962,0.009245584,0.0002339617,0.0001646005,0.0001309148,0.002816539,0.0000773808,0.0005612363],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01003209,"threshold_uncertainty_score":0.05305541,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1988634726657897,"score_gpt":0.6044226772035658,"score_spread":0.4055592045377761,"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."}}