{"id":"W2485506907","doi":"10.4018/978-1-4666-8838-4.ch013","title":"Mobile Journalism and Innovation","year":2015,"lang":"en","type":"book-chapter","venue":"Advances in multimedia and interactive technologies book series","topic":"University-Industry-Government Innovation Models","field":"Business, Management and Accounting","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Affordance; Journalism; Representativeness heuristic; Narrative; Technological convergence; Convergence (economics); Sample (material); Political science; Sociology; Computer science; Humanities; Media studies; Art; Psychology; Human–computer interaction; Telecommunications; Literature; Social psychology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001606369,0.0003649014,0.0003902364,0.001314891,0.0001066245,0.0001431632,0.0002368711,0.0005514712,0.0001417208],"category_scores_gemma":[0.0002380247,0.0003568791,0.00002469107,0.0002648518,0.0005310649,0.00847775,0.0005701183,0.001076333,0.00002172324],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001791812,"about_ca_system_score_gemma":0.00002506402,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001082307,"about_ca_topic_score_gemma":0.00002451771,"domain_scores_codex":[0.9986893,0.000003530063,0.0004101508,0.0004135644,0.0002769427,0.0002065161],"domain_scores_gemma":[0.9986432,0.00005931512,0.0006640409,0.0002093509,0.0004165282,0.000007554207],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002701455,0.00003726901,0.001755915,0.0001890355,0.00008116954,0.00006736781,0.0002647843,0.00002957962,0.0002299137,0.7704386,0.004623118,0.2220131],"study_design_scores_gemma":[0.0004328164,0.00005814843,0.0000308334,0.0003106695,0.00002547738,0.000009552476,0.002336663,0.0002644289,0.000139723,0.0848436,0.9111818,0.0003662562],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.007421169,0.01835318,0.0005581355,0.001945126,0.001005589,0.001253226,0.00005531823,0.0009053483,0.9685029],"genre_scores_gemma":[0.1703664,0.05896154,0.007877172,0.00264392,0.001203623,0.0002229149,0.000382185,0.0002958171,0.7580464],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.9065587,"threshold_uncertainty_score":0.9998883,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02185827563457176,"score_gpt":0.2593488422669405,"score_spread":0.2374905666323687,"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."}}