{"id":"W4301049521","doi":"","title":"Le chercheur « au terrain », des enjeux multiples","year":2016,"lang":"fr","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Cultural Insights and Digital Impacts","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Multiple; Computer science; Terrain; Geography; Arithmetic; Mathematics","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":[0.004066445,0.000900868,0.0006983326,0.001700651,0.01146353,0.01476124,0.001452196,0.004793078,0.01441978],"category_scores_gemma":[0.01566725,0.0004620333,0.000728531,0.00203555,0.0175145,0.01395002,0.00694108,0.009781054,0.002495395],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00704327,"about_ca_system_score_gemma":0.004764898,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02398632,"about_ca_topic_score_gemma":0.02789652,"domain_scores_codex":[0.9941334,0.002661022,0.0001210097,0.0007357155,0.001820139,0.0005286082],"domain_scores_gemma":[0.9949137,0.001880698,0.0003603424,0.0008488009,0.001195475,0.000800964],"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":[0.00009479779,0.00003328083,0.001513486,0.000147188,0.00002731523,0.001655811,0.06256155,0.0006631538,0.001568012,0.857244,0.02618944,0.04830199],"study_design_scores_gemma":[0.00002037684,0.00002927517,0.002011596,0.0002310572,0.00001343576,0.001969745,0.0435894,0.0007531295,0.0007937704,0.0847993,0.865728,0.00006100271],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.1547967,0.01738439,0.07064356,0.1695224,0.00923474,0.00009112889,0.0003483284,0.0006220661,0.5773566],"genre_scores_gemma":[0.7795935,0.005526147,0.01436395,0.006455008,0.001930583,0.0001005205,0.0001339688,0.0006764016,0.1912199],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9885365,"threshold_uncertainty_score":0.05110276,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1502887078585587,"score_gpt":0.2876673147813649,"score_spread":0.1373786069228062,"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."}}