{"id":"W2756489727","doi":"10.1007/978-3-319-66435-4_3","title":"Understanding the Crowd: Ethical and Practical Matters in the Academic Use of Crowdsourcing","year":2017,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Mobile Crowdsensing and Crowdsourcing","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Crowdsourcing; Churning; Computer science; Parallels; Data science; Knowledge management; World Wide Web; Engineering","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","scholarly_communication","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.004224873,0.0004162718,0.0004850252,0.000450465,0.0007297895,0.001461084,0.002437498,0.0006394856,0.000002502316],"category_scores_gemma":[0.0007805847,0.0002631854,0.0001039056,0.0002760458,0.002558351,0.0006783961,0.001341044,0.004042296,0.000003044523],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002139124,"about_ca_system_score_gemma":0.0003778316,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007022281,"about_ca_topic_score_gemma":0.00012912,"domain_scores_codex":[0.9963847,0.0002150607,0.0005926475,0.001060929,0.001113906,0.0006326882],"domain_scores_gemma":[0.9927351,0.004815194,0.000498386,0.001759093,0.00009269204,0.0000995201],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00009716408,0.000105536,0.00201405,0.0004918457,0.00009818411,0.001606759,0.07358242,0.1060166,0.002126077,0.5267303,0.001611643,0.2855195],"study_design_scores_gemma":[0.000562384,0.0001913436,0.001349434,0.002652572,0.00004384415,0.00155187,0.00002349121,0.7211674,0.0006266962,0.2673979,0.003356319,0.001076744],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0009294497,0.0001430406,0.9664643,0.03109216,0.0005953784,0.0003500303,0.000001488097,0.00003816963,0.0003860284],"genre_scores_gemma":[0.9390552,0.00007113192,0.04859206,0.01193037,0.0002511879,0.000005522247,7.003845e-7,0.00002967594,0.0000641363],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9381258,"threshold_uncertainty_score":0.9999821,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1213857501714136,"score_gpt":0.3189866619977965,"score_spread":0.1976009118263829,"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."}}