{"id":"W4412361240","doi":"10.1080/13511610.2025.2527104","title":"Why context matters: understanding transdisciplinary research through the lens of nine context factors","year":2025,"lang":"en","type":"article","venue":"Innovation The European Journal of Social Science Research","topic":"Sustainability and Climate Change Governance","field":"Environmental Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Brock University","funders":"Bundesministerium für Bildung und Forschung","keywords":"Context (archaeology); Sociology; Lens (geology); Transdisciplinarity; Engineering ethics; Through-the-lens metering; Epistemology; Political science; Social science; Engineering; Geography; Philosophy","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.02384133,0.001745406,0.001174575,0.008687509,0.01104618,0.02210607,0.00225654,0.004095772,0.003165422],"category_scores_gemma":[0.02420294,0.0009996921,0.00104362,0.007429455,0.05363301,0.03017293,0.01493349,0.005828287,0.0003443589],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01055408,"about_ca_system_score_gemma":0.01015291,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007154391,"about_ca_topic_score_gemma":0.01001115,"domain_scores_codex":[0.9673931,0.02756375,0.0008929559,0.001677466,0.00158443,0.0008882916],"domain_scores_gemma":[0.9655972,0.02811617,0.001945707,0.001878516,0.00134003,0.001122449],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"qualitative","study_design_scores_codex":[0.00003691405,0.00002887123,0.003569484,0.0008108508,0.00004937987,0.001033768,0.3939144,0.0005492537,0.0009008083,0.5748185,0.001045955,0.02324186],"study_design_scores_gemma":[0.00002137441,0.00004076992,0.002748168,0.002184339,0.00007215006,0.0007985526,0.3398851,0.001176798,0.0005679931,0.540087,0.1123727,0.00004504064],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1820025,0.04045155,0.4427135,0.08146056,0.001389958,0.0008920272,0.0003809225,0.0002144331,0.2504945],"genre_scores_gemma":[0.9133278,0.008714394,0.07150925,0.002553195,0.0002011086,0.0006355108,0.0001114658,0.0001275803,0.002819685],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9761587,"threshold_uncertainty_score":0.1260865,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2820054168403672,"score_gpt":0.4272612976956808,"score_spread":0.1452558808553136,"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."}}