{"id":"W2140863794","doi":"10.5555/2017434.2017440","title":"Interpretation of formative measurement in information systems research","year":2009,"lang":"en","type":"article","venue":"Journal of the Association for Information Systems","topic":"Technology Adoption and User Behaviour","field":"Decision Sciences","cited_by":708,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; University of British Columbia","funders":"","keywords":"Formative assessment; Multicollinearity; Nomological network; Construct (python library); CLARITY; Interpretation (philosophy); Structural equation modeling; Computer science; Psychology; Mathematics education; Machine learning; Regression analysis","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":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.03238757,0.0001066606,0.0004050625,0.001757992,0.0001699575,0.0004317196,0.0008222919,0.0002295976,0.000001794225],"category_scores_gemma":[0.01133242,0.0000690724,0.0002085465,0.001485964,0.00002878304,0.006103666,0.00004345055,0.0004071973,0.0000423108],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001381598,"about_ca_system_score_gemma":0.0002406396,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002313048,"about_ca_topic_score_gemma":0.000006485068,"domain_scores_codex":[0.9913695,0.0006566399,0.003377418,0.00006303874,0.004298421,0.0002349635],"domain_scores_gemma":[0.9831773,0.000619241,0.005950978,0.0002919398,0.009911761,0.0000487926],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.001921883,0.0005991011,0.1994005,0.0004108047,0.0003795335,7.344414e-7,0.1228811,0.180183,0.0009614858,0.2477993,0.157713,0.08774947],"study_design_scores_gemma":[0.007476641,0.001355336,0.5838239,0.001388209,0.0000878292,0.00006973476,0.09589878,0.08352176,0.001129125,0.01081954,0.2139583,0.0004708293],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8007053,0.0005807878,0.1390095,0.009773301,0.0155017,0.007069979,0.000273502,0.0001165759,0.0269694],"genre_scores_gemma":[0.9996336,0.000008757825,0.00007643539,0.00007767777,0.00004434622,0.00002057343,0.000004376664,0.000002436961,0.0001318245],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3844234,"threshold_uncertainty_score":0.9969956,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1409228907130452,"score_gpt":0.4076226579390167,"score_spread":0.2666997672259714,"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."}}