{"id":"W1499244104","doi":"10.1007/978-3-642-24469-8_27","title":"Making Sense in the Margins: A Field Study of Annotation","year":2011,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Text Analysis Techniques","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"Nipissing University; Dalhousie University","funders":"","keywords":"Annotation; Computer science; Hypertext; Field (mathematics); Style (visual arts); Information retrieval; World Wide Web; Artificial intelligence","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":[0.01501988,0.0004400952,0.0004122054,0.003193404,0.007081574,0.005639825,0.001904649,0.001775434,0.007882996],"category_scores_gemma":[0.07626405,0.0005273577,0.0002429818,0.003582717,0.01029164,0.01371969,0.005095149,0.003039216,0.001079064],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002540555,"about_ca_system_score_gemma":0.002609322,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005483998,"about_ca_topic_score_gemma":0.005241859,"domain_scores_codex":[0.9879253,0.008716979,0.0004250259,0.001253941,0.001297502,0.0003812572],"domain_scores_gemma":[0.8874903,0.0952747,0.003543862,0.006075073,0.006402519,0.001213475],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.0002350333,0.0001586225,0.009791281,0.0003156302,0.000007254207,0.0002778965,0.879454,0.0001062591,0.004494444,0.04720006,0.002768111,0.05519137],"study_design_scores_gemma":[0.00005358386,0.0002530643,0.02732855,0.000831659,0.00003585257,0.0009200649,0.7751671,0.002515726,0.007628857,0.07931697,0.1058497,0.00009884929],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8345612,0.001176567,0.03251556,0.003430254,0.0001643025,0.0004082122,0.000308578,0.0001926319,0.1272427],"genre_scores_gemma":[0.9836503,0.0002684175,0.006212628,0.0002570958,0.00005053695,0.0001427721,0.0001630056,0.0001748885,0.009080327],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01501988,"threshold_uncertainty_score":0.07943368,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.040288491415573,"score_gpt":0.3078085936347314,"score_spread":0.2675201022191584,"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."}}