{"id":"W3202745629","doi":"10.1016/j.techfore.2021.121139","title":"Towards an anticipatory system incorporating corporate foresight and competitive intelligence in creating knowledge: a longitudinal Moroccan bank case study","year":2021,"lang":"en","type":"article","venue":"Technological Forecasting and Social Change","topic":"Competitive and Knowledge Intelligence","field":"Business, Management and Accounting","cited_by":31,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Ottawa","funders":"National Research University Higher School of Economics","keywords":"Futures studies; Competitive intelligence; Futures contract; Context (archaeology); Competitive advantage; Market intelligence; Business; Knowledge management; Unit (ring theory); Knowledge economy; Strategic business unit; Industrial organization; Marketing; Economics; Computer science; Artificial intelligence; Finance","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008907238,0.0003150472,0.0005081869,0.0002926881,0.0007603663,0.0002986231,0.000171029,0.0001938989,0.00002218418],"category_scores_gemma":[0.0003349761,0.0002890201,0.00004973057,0.0009587011,0.0003939239,0.0005211618,0.0006654112,0.0004393698,0.000007665712],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008365232,"about_ca_system_score_gemma":0.0000344395,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001248628,"about_ca_topic_score_gemma":0.007177995,"domain_scores_codex":[0.9981757,0.0000919487,0.0004685527,0.0006735143,0.0001687335,0.0004215358],"domain_scores_gemma":[0.9989761,0.0001475146,0.0003223661,0.0001479454,0.0003736748,0.00003242329],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"qualitative","study_design_scores_codex":[0.00005154955,0.0007101382,0.4629644,0.0007445113,0.00004150509,0.01289913,0.008688661,0.000002459939,0.00008138859,0.364143,0.000002591702,0.1496708],"study_design_scores_gemma":[0.001640804,0.0008358731,0.09886689,0.00215971,0.0003323006,0.002971161,0.7117559,0.1525398,0.0005493443,0.02558822,0.0003490594,0.002410837],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9924823,0.0005472219,0.0006663226,0.00007014637,0.0001079094,0.0004751551,0.000005702062,0.0003217,0.005323545],"genre_scores_gemma":[0.9988574,0.000008852736,0.0003252503,0.00005131098,0.0005843071,0.0001205842,0.00001151748,0.00002492055,0.00001585475],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7030673,"threshold_uncertainty_score":0.9999562,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2103282572287286,"score_gpt":0.3216846916220962,"score_spread":0.1113564343933676,"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."}}