{"id":"W2592640428","doi":"10.1007/978-3-319-55134-0_22","title":"Investigation of Social Predictors of Competitive Behavior in Persuasive Technology","year":2017,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Digital Marketing and Social Media","field":"Social Sciences","cited_by":19,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Competition (biology); Context (archaeology); Persuasive technology; Psychology; Social learning; Sample (material); Social media; Computer science; Variation (astronomy); Social influence; Social psychology; Persuasion; Knowledge management; World Wide Web","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":[0.002694328,0.0003404284,0.0003072265,0.001320924,0.0009246445,0.001962941,0.0005489057,0.001142818,0.004267637],"category_scores_gemma":[0.02896126,0.0003170019,0.0004088493,0.001302618,0.0005933517,0.001034476,0.0007035694,0.001360514,0.0006398731],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005016355,"about_ca_system_score_gemma":0.000576024,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002697073,"about_ca_topic_score_gemma":0.00398033,"domain_scores_codex":[0.9975438,0.001518301,0.0001078378,0.0001686635,0.0004724506,0.0001890025],"domain_scores_gemma":[0.9301197,0.0571874,0.006807574,0.001142381,0.002370851,0.002372252],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003956899,0.001318099,0.9805015,0.00003990249,0.0001572146,0.0001084933,0.002126724,0.0002250824,0.000528522,0.0006879058,0.0002458252,0.01366508],"study_design_scores_gemma":[0.00001719592,0.0004591364,0.9924642,0.00002528979,0.000112349,0.0001454803,0.00268876,0.002582125,0.0004273679,0.0006734674,0.0003888808,0.00001574796],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9966918,0.0001278115,0.000181759,0.0001012266,0.000007579563,0.00001323904,0.00003983781,0.000004374161,0.002832299],"genre_scores_gemma":[0.9991286,0.00006725395,0.0001938408,0.00001595037,0.00001616698,0.00002016603,0.00005375582,0.000004053321,0.00050009],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004267637,"threshold_uncertainty_score":0.01427668,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01914641009859816,"score_gpt":0.2773327695604986,"score_spread":0.2581863594619004,"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."}}