{"id":"W6946148696","doi":"10.3389/fpubh.2023.982908.s001","title":"Data_Sheet_1_Qualitative analysis of front-of package labeling policy interactions between stakeholders and Health Canada.PDF","year":2023,"lang":"en","type":"dataset","venue":"Figshare","topic":"Evolution and Paleontology Studies","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Stakeholder; Thematic analysis; Unintended consequences; Scope (computer science); Government (linguistics); Policy analysis; Health policy; Stakeholder analysis; Corporate governance","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":[0.02410933,0.0008249148,0.001113642,0.008358631,0.01230828,0.006946397,0.00284519,0.001761534,0.1703868],"category_scores_gemma":[0.04761053,0.001125438,0.0009531343,0.01799051,0.004487684,0.002406511,0.004943448,0.002543753,0.015565],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.06158322,"about_ca_system_score_gemma":0.1378753,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.6815037,"about_ca_topic_score_gemma":0.7231475,"domain_scores_codex":[0.985165,0.005252849,0.001088835,0.001595649,0.004793875,0.002103946],"domain_scores_gemma":[0.9333777,0.03174179,0.001839764,0.003522343,0.02695258,0.002565756],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"qualitative","study_design_scores_codex":[0.0003539129,0.0002676047,0.007467378,0.009193488,0.00002519863,0.0005678325,0.1517249,0.0007917547,0.001066201,0.02814656,0.6720776,0.1283175],"study_design_scores_gemma":[0.0001898238,0.00005256018,0.01388181,0.00460346,0.00002445605,0.00008293905,0.173374,0.0003985818,0.001270753,0.003728106,0.8022685,0.0001249697],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.03226953,0.0008798697,0.01213052,0.01454893,0.0008045097,0.06153249,0.7141748,0.0007881936,0.1628711],"genre_scores_gemma":[0.146125,0.003004247,0.09111077,0.01279188,0.0003015665,0.4131339,0.1731718,0.000835689,0.1595253],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.9384168,"threshold_uncertainty_score":0.6407441,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2378786272491175,"score_gpt":0.3738903952841739,"score_spread":0.1360117680350565,"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."}}