{"id":"W2758823016","doi":"10.17351/ests2017.134","title":"<b>Making and Doing Politics Through Grassroots Scientific Research on the Energy and Petrochemical Industries</b>","year":2017,"lang":"en","type":"article","venue":"Engaging Science Technology and Society","topic":"Innovative Human-Technology Interaction","field":"Computer Science","cited_by":67,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Grassroots; Scholarship; Work (physics); Politics; Business; Petrochemical; Environmental planning; Political science; Engineering; Waste management; Environmental science","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":["sts"],"consensus_categories":[],"category_scores_codex":[0.008996068,0.000474465,0.0003039849,0.001981573,0.007090497,0.01502802,0.000918373,0.003187998,0.03202235],"category_scores_gemma":[0.009609347,0.0002889038,0.0004281833,0.002697656,0.02055865,0.01134113,0.008475264,0.003833751,0.006057301],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.007525154,"about_ca_system_score_gemma":0.01050417,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005112056,"about_ca_topic_score_gemma":0.01119447,"domain_scores_codex":[0.9939657,0.003814229,0.0001481374,0.0005200467,0.001046015,0.0005059622],"domain_scores_gemma":[0.9895644,0.005661196,0.000533496,0.001783522,0.0009625634,0.001494758],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"qualitative","study_design_scores_codex":[0.0000452965,0.00006610468,0.002187606,0.0003709115,0.00001792532,0.0002197163,0.03838786,0.0007179675,0.002029957,0.6964982,0.09155381,0.1679046],"study_design_scores_gemma":[0.000009464972,0.00002939953,0.001924547,0.0004904174,0.000009166271,0.0001003736,0.03431247,0.0008400726,0.001556153,0.2033459,0.7573594,0.00002258491],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.03925914,0.005638156,0.04535569,0.154495,0.003243275,0.0001709647,0.000205643,0.0003892595,0.7512428],"genre_scores_gemma":[0.7472679,0.01060256,0.03027654,0.01431346,0.001117955,0.0002482661,0.0002574866,0.0006978909,0.195218],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9929095,"threshold_uncertainty_score":0.1071255,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06759164080097939,"score_gpt":0.35983839599914,"score_spread":0.2922467551981606,"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."}}