{"id":"W6920856770","doi":"10.6084/m9.figshare.22518004","title":"United we stood, divided we transform? Exploring coalition transformation divergence in the EU trade policy field","year":2023,"lang":"en","type":"article","venue":"Figshare","topic":"European Union Policy and Governance","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Divergence (linguistics); Ideology; Politics; Order (exchange); Trade union; Field (mathematics); Civil society; Opportunity structures; Commercial policy","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03105305,0.0003968263,0.0008542593,0.003863925,0.01440392,0.01961369,0.00207415,0.003071481,0.003876194],"category_scores_gemma":[0.04021572,0.0004499095,0.0004040071,0.004352419,0.02667609,0.01426236,0.01860406,0.004376779,0.000288341],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01096595,"about_ca_system_score_gemma":0.007869927,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01076284,"about_ca_topic_score_gemma":0.0106894,"domain_scores_codex":[0.9718882,0.02142089,0.0006268147,0.001567307,0.001464635,0.003032109],"domain_scores_gemma":[0.9728062,0.02062508,0.002093904,0.001154837,0.001353915,0.001966033],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"observational","study_design_scores_codex":[0.00005524182,0.00004945634,0.007383935,0.0000727738,0.00001562622,0.0002415143,0.9108237,0.0001115779,0.0001986924,0.06827057,0.0006330936,0.01214372],"study_design_scores_gemma":[0.00001098145,0.00002032489,0.005678665,0.0001261723,0.000004855162,0.0000703416,0.9598663,0.0002142365,0.0001046887,0.01754409,0.01634627,0.00001304091],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9286705,0.001240689,0.003766181,0.01093954,0.0001124564,0.00007975416,0.00004693336,0.00001437258,0.0551296],"genre_scores_gemma":[0.998085,0.0001967856,0.0003289692,0.0004059019,0.000009426963,0.00003859857,0.00002698498,0.00001432247,0.000894042],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03105305,"threshold_uncertainty_score":0.1642262,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1991280471216702,"score_gpt":0.3464501570953468,"score_spread":0.1473221099736765,"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."}}