{"id":"W7114782143","doi":"10.4000/15bo7","title":"Promesses technomnésiques et datafication de la mémoire personnelle","year":2025,"lang":"fr","type":"article","venue":"Communication","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; Université du Québec à Trois-Rivières","funders":"","keywords":"ESPACE; Normative; Public space","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0159827,0.00127501,0.0006905793,0.003684084,0.002115351,0.01069069,0.002667157,0.002080258,0.01127819],"category_scores_gemma":[0.06273516,0.001038233,0.001099148,0.002547616,0.004564434,0.01028705,0.005449502,0.002872442,0.00464429],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002781578,"about_ca_system_score_gemma":0.004405953,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003826041,"about_ca_topic_score_gemma":0.003517925,"domain_scores_codex":[0.9802208,0.007148573,0.001310058,0.002575752,0.008004392,0.0007404144],"domain_scores_gemma":[0.9476318,0.03283217,0.003380967,0.008762939,0.006607613,0.0007844998],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002892878,0.0001453779,0.008665658,0.001850071,0.0001151383,0.0003587402,0.01494931,0.009615611,0.01804778,0.368012,0.005347763,0.5726034],"study_design_scores_gemma":[0.00009137005,0.0006151424,0.01055913,0.001996755,0.000201431,0.001831431,0.008894841,0.0560029,0.06340201,0.2396823,0.6163887,0.0003340584],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04986878,0.002956549,0.8667448,0.003563626,0.0003973398,0.0005276945,0.0003228474,0.00193923,0.07367907],"genre_scores_gemma":[0.3822644,0.003511401,0.5545215,0.0006143138,0.0003496404,0.001019119,0.0007854603,0.0009255863,0.05600862],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0159827,"threshold_uncertainty_score":0.08452564,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1829328790002892,"score_gpt":0.3871798191012422,"score_spread":0.204246940100953,"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."}}