{"id":"W2102770113","doi":"10.19173/irrodl.v13i1.1073","title":"Toward a CoI population parameter: The impact of unit (sentence vs. message) on the results of quantitative content analysis","year":2012,"lang":"en","type":"article","venue":"The International Review of Research in Open and Distributed Learning","topic":"Online and Blended Learning","field":"Social Sciences","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Sentence; Population; Unit (ring theory); Meaning (existential); Distance education; Psychology; Mathematics education; Computer science; Artificial intelligence; Sociology; Demography","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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.01644099,0.0000725424,0.0002860471,0.0001263409,0.0002328304,0.00006183669,0.0008705743,0.00002911424,0.0001145473],"category_scores_gemma":[0.01545737,0.00003345481,0.0001405147,0.001242705,0.0004146037,0.0001965742,0.0002443661,0.0004715386,0.000002317494],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007838241,"about_ca_system_score_gemma":0.0001684235,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02182046,"about_ca_topic_score_gemma":0.0003189927,"domain_scores_codex":[0.9955296,0.002762552,0.0004651181,0.0001211896,0.0009041543,0.000217398],"domain_scores_gemma":[0.993299,0.00542193,0.0004542248,0.0001821085,0.0005952319,0.00004747699],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00110099,0.0003602409,0.8408381,0.0002786065,0.000907505,0.000002060605,0.006676527,0.002263131,0.00008642391,0.1236311,0.0008369753,0.02301834],"study_design_scores_gemma":[0.0009661887,0.0006036636,0.8886868,0.00640743,0.0001854932,0.000001789783,0.08156719,0.005686396,0.0001307412,0.001857277,0.01373449,0.0001725058],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9694155,0.003983386,0.0001283129,0.02119531,0.00003374977,0.0007524278,0.0001237673,0.00000398374,0.004363566],"genre_scores_gemma":[0.9904693,0.009171283,0.00005915442,0.00003145386,0.0000226677,0.00001685332,0.00009629812,0.000003025069,0.0001299097],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1217738,"threshold_uncertainty_score":0.9928358,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3258906205764565,"score_gpt":0.5306512924549023,"score_spread":0.2047606718784458,"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."}}