{"id":"W2042000703","doi":"10.1108/03090560810877196","title":"Using open source data in developing competitive and marketing intelligence","year":2008,"lang":"en","type":"article","venue":"European Journal of Marketing","topic":"Competitive and Knowledge Intelligence","field":"Business, Management and Accounting","cited_by":115,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Competitive intelligence; Business intelligence; Marketing research; Originality; Marketing; Marketing strategy; Competitive advantage; Knowledge management; Marketing management; Process (computing); Computer science; Digital marketing; Business; Data science; Qualitative research; Sociology","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":[],"consensus_categories":[],"category_scores_codex":[0.01741243,0.0002079666,0.0003401482,0.0003779588,0.0003143776,0.0003642729,0.001504789,0.00001995766,0.0001183124],"category_scores_gemma":[0.005016889,0.000199794,0.00004358194,0.0005173383,0.0001383117,0.001801723,0.003022925,0.0003801989,0.00003788318],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005144422,"about_ca_system_score_gemma":0.00007682827,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000493821,"about_ca_topic_score_gemma":0.00004012132,"domain_scores_codex":[0.997414,0.0007555244,0.0008851514,0.0003555466,0.0002656974,0.0003240893],"domain_scores_gemma":[0.9975229,0.001013554,0.0007873451,0.0002823141,0.0003653567,0.00002854794],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002922198,0.0002640061,0.592603,0.0008557496,0.0001756579,0.005279249,0.001544523,0.0006753243,0.001694477,0.005805556,0.001940039,0.3862402],"study_design_scores_gemma":[0.001398997,0.00004827636,0.6072666,0.01056826,0.00009868613,0.001934608,0.009022862,0.03119609,0.00020823,0.0002926367,0.3365746,0.001390125],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7694519,0.001550823,0.0448053,0.000482485,0.0004937243,0.0002493103,0.00000191787,0.0000314254,0.1829331],"genre_scores_gemma":[0.9830946,0.0003022087,0.01503529,0.0004133784,0.0009506667,2.931e-7,0.000003045946,0.00004855736,0.0001519877],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3848501,"threshold_uncertainty_score":0.8147362,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.134245647079297,"score_gpt":0.3042599098405597,"score_spread":0.1700142627612627,"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."}}