{"id":"W1994644260","doi":"10.1109/re.2012.6345835","title":"On the usage of context for requirements elicitation: End-user involvement in IT ecosystems","year":2012,"lang":"en","type":"article","venue":"","topic":"Advanced Software Engineering Methodologies","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Requirements elicitation; Computer science; Stakeholder; Context (archaeology); Requirements engineering; Adaptation (eye); Scalability; Requirements management; End user; Requirements analysis; User requirements document; Knowledge management; Process management; Risk analysis (engineering); World Wide Web; Software engineering; Business; Database; Software","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":[],"consensus_categories":[],"category_scores_codex":[0.03520519,0.001167045,0.0007379263,0.002632839,0.003716634,0.005794054,0.002056002,0.002998151,0.002012231],"category_scores_gemma":[0.07527884,0.0008653142,0.0007454143,0.002620021,0.005626065,0.009280118,0.007778552,0.002735958,0.0005932514],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003115864,"about_ca_system_score_gemma":0.003227256,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00297294,"about_ca_topic_score_gemma":0.005484154,"domain_scores_codex":[0.9100913,0.07880317,0.002391111,0.002160107,0.005492319,0.001062062],"domain_scores_gemma":[0.8546739,0.1291792,0.003410305,0.005015052,0.006826001,0.0008955781],"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":[0.0007074119,0.0005007074,0.01377216,0.004022856,0.0001312188,0.003057709,0.2767709,0.01123418,0.02671944,0.2577322,0.004928887,0.4004224],"study_design_scores_gemma":[0.0003313429,0.002129999,0.0217851,0.009402029,0.0003459429,0.007409635,0.2627918,0.1013229,0.03138691,0.2979843,0.2643706,0.0007395127],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1352657,0.001599184,0.7992628,0.009639022,0.0001174961,0.002449444,0.000139636,0.0002903441,0.05123632],"genre_scores_gemma":[0.5557918,0.001723344,0.4365684,0.001714003,0.00006896218,0.001688692,0.0001433732,0.0001187284,0.002182672],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03520519,"threshold_uncertainty_score":0.1861851,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1348021712276041,"score_gpt":0.343437337813205,"score_spread":0.2086351665856009,"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."}}