{"id":"W4396833268","doi":"10.1145/3613905.3649126","title":"Casting Connections: A Fishy Approach to Conference Engagement","year":2024,"lang":"en","type":"article","venue":"","topic":"Conferences and Exhibitions Management","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Adventure; Metaphor; Hook; Fishing; Computer science; Fish <Actinopterygii>; Customer engagement; Sociology; World Wide Web; Engineering; Social media; Fishery; 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":[0.01148233,0.0009830203,0.0005435365,0.004306305,0.01504023,0.02014846,0.003974459,0.005165307,0.04575896],"category_scores_gemma":[0.02794713,0.000872127,0.001132088,0.002609526,0.01380087,0.01687456,0.02830651,0.006154395,0.00642423],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005592179,"about_ca_system_score_gemma":0.005544738,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003450945,"about_ca_topic_score_gemma":0.008403952,"domain_scores_codex":[0.9797473,0.01507126,0.0003968846,0.00166759,0.002139827,0.0009771908],"domain_scores_gemma":[0.9866886,0.006367827,0.0008400026,0.002425314,0.00127872,0.002399563],"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":[0.0001078218,0.0001732336,0.001813054,0.0004534675,0.00004435142,0.0005574167,0.1830309,0.0009592938,0.002328415,0.7143081,0.02246206,0.07376191],"study_design_scores_gemma":[0.00007401931,0.0001954698,0.0009520437,0.0005088776,0.00005388841,0.0005005128,0.08540681,0.004301404,0.0009747267,0.3052805,0.601662,0.0000897264],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.03257397,0.0009104492,0.3724581,0.03364132,0.001929814,0.001501434,0.00024392,0.001237698,0.5555032],"genre_scores_gemma":[0.5689735,0.001003089,0.2313028,0.006700585,0.0008172006,0.004575838,0.0002764045,0.001196324,0.1851542],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.04575896,"threshold_uncertainty_score":0.153079,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1321808897169959,"score_gpt":0.352275595867576,"score_spread":0.2200947061505801,"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."}}