{"id":"W6946359867","doi":"10.34745/numerev_1760","title":"Chercher l’humain dans l’institutionnel : étude des messages officiels publiés sur les sites web du gouvernement fédéral canadien","year":2019,"lang":"fr","type":"article","venue":"NumeRev","topic":"Cultural Insights and Digital Impacts","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Context (archaeology); Publics; Performative utterance; Web technology","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004223852,0.0004579423,0.0004175221,0.0000966049,0.0004472082,0.001919349,0.0009396949,0.0002403509,0.001567649],"category_scores_gemma":[0.000253986,0.0003644437,0.0002168904,0.0005651889,0.0004197785,0.004114733,0.000438518,0.0003228389,0.000685124],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008159124,"about_ca_system_score_gemma":0.0006370327,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.0385321,"about_ca_topic_score_gemma":0.09828394,"domain_scores_codex":[0.9972205,0.0001524553,0.0004566574,0.0006893704,0.0004950488,0.000985977],"domain_scores_gemma":[0.998091,0.0001358226,0.0001770016,0.0006303076,0.000435931,0.0005299313],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0000397405,0.001456639,0.08658573,0.0005786789,0.0004299147,0.0004877398,0.03292729,0.002618218,0.01507168,0.7345316,0.0857777,0.03949503],"study_design_scores_gemma":[0.002860722,0.0009268937,0.2390276,0.001051579,0.0001552069,0.0004744846,0.003455083,0.03574578,0.004575055,0.007691731,0.7016779,0.00235802],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8129585,0.00645934,0.001383507,0.004801534,0.001314281,0.0004054718,0.0000710338,0.0001403315,0.172466],"genre_scores_gemma":[0.9348772,0.0003395597,0.0008141293,0.000606732,0.0002695999,0.00001090882,0.00002498785,0.00002507061,0.06303187],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7268399,"threshold_uncertainty_score":0.9998807,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1657225346472989,"score_gpt":0.2739021054201784,"score_spread":0.1081795707728795,"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."}}