{"id":"W4290803193","doi":"10.1145/3549015.3555674","title":"ENAGRAM: An App to Evaluate Preventative Nudges for Instagram","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Privacy, Security, and Data Protection","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Nudge theory; Computer science; Mobile apps; World Wide Web; Psychology; Social psychology","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":[],"consensus_categories":[],"category_scores_codex":[0.003761491,0.001188265,0.0004880895,0.001123074,0.0004426938,0.001035835,0.0009288653,0.001232454,0.007420291],"category_scores_gemma":[0.02043078,0.000349658,0.0005048162,0.0002935953,0.0004906806,0.00185955,0.001610433,0.0007573302,0.00184583],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002886907,"about_ca_system_score_gemma":0.0003533219,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003691342,"about_ca_topic_score_gemma":0.0009273805,"domain_scores_codex":[0.9967668,0.002037009,0.0002323753,0.0003236316,0.0004979491,0.0001421444],"domain_scores_gemma":[0.9735661,0.02183626,0.001546099,0.001630484,0.0009287164,0.0004923444],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.01015206,0.01950044,0.1402455,0.007642622,0.000572571,0.002116289,0.02686746,0.005295707,0.03084719,0.007447646,0.06558013,0.6837325],"study_design_scores_gemma":[0.002975888,0.03715586,0.5154661,0.00303015,0.001174857,0.003676382,0.01813626,0.06851472,0.06477543,0.02327694,0.2606434,0.001173983],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.93699,0.0005526877,0.02796483,0.0007163004,0.000223566,0.005912638,0.00405534,0.00803158,0.0155529],"genre_scores_gemma":[0.8753182,0.0004856901,0.09984747,0.000779534,0.0001125124,0.009855418,0.002874949,0.000500024,0.01022611],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007420291,"threshold_uncertainty_score":0.02482337,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09692521396755932,"score_gpt":0.42856875749354,"score_spread":0.3316435435259807,"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."}}