{"id":"W1937849680","doi":"10.19173/irrodl.v15i4.1785","title":"Crowdteaching: Supporting teaching as designing in collective intelligence communities","year":2014,"lang":"en","type":"article","venue":"The International Review of Research in Open and Distributed Learning","topic":"E-Learning and Knowledge Management","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Utah State University; American Educational Research Association; National Science Foundation","keywords":"Collective intelligence; Crowdsourcing; Leverage (statistics); Citizen journalism; Curriculum; Context (archaeology); Mathematics education; Computer science; Quality (philosophy); Knowledge management; World Wide Web; Psychology; Pedagogy","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003548873,0.0007949061,0.0004088191,0.001414223,0.003072888,0.004242554,0.002748674,0.002229585,0.007007839],"category_scores_gemma":[0.008974268,0.0005332573,0.0007224301,0.0008889773,0.0035383,0.004913163,0.009512331,0.001446975,0.001795193],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008513027,"about_ca_system_score_gemma":0.002467437,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002374645,"about_ca_topic_score_gemma":0.003324396,"domain_scores_codex":[0.9975126,0.001309922,0.0000912068,0.000442919,0.0004258275,0.0002175703],"domain_scores_gemma":[0.9927047,0.003587963,0.000492983,0.001593822,0.0003661923,0.001254382],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"qualitative","study_design_scores_codex":[0.0004717937,0.002568749,0.01304086,0.001280589,0.0001065685,0.001910633,0.06556061,0.02985864,0.04511916,0.09312495,0.03924087,0.7077166],"study_design_scores_gemma":[0.0003919422,0.0007917911,0.008587061,0.0004965576,0.00009525377,0.001168885,0.02485788,0.1534241,0.02554582,0.2231851,0.5611988,0.0002568692],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.15718,0.0006035943,0.7743978,0.003263794,0.000244966,0.001506997,0.0002723088,0.01237784,0.05015274],"genre_scores_gemma":[0.5052908,0.0004715537,0.4652188,0.0006452168,0.0001107349,0.001572347,0.0006378674,0.0009727206,0.02507997],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007007839,"threshold_uncertainty_score":0.02344358,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09887689298514886,"score_gpt":0.4426582626714609,"score_spread":0.3437813696863121,"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."}}