{"id":"W3171635994","doi":"10.69645/tuuj4791","title":"Negotiating the sweet spot: virtual negotiations","year":2021,"lang":"en","type":"article","venue":"˜The œbusiness & management collection.","topic":"European Union Policy and Governance","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Kellogg's (Canada)","funders":"","keywords":"Sweet spot; Negotiation; Political science; Computer science; Simulation; Law","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":[0.01229483,0.0006728958,0.0006456911,0.001381884,0.008474627,0.02246036,0.002428613,0.00535705,0.04712849],"category_scores_gemma":[0.02677252,0.0006934164,0.0005920293,0.001999179,0.01107341,0.02741344,0.01720545,0.004384505,0.004074777],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002405473,"about_ca_system_score_gemma":0.003885003,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001294374,"about_ca_topic_score_gemma":0.001639711,"domain_scores_codex":[0.9887909,0.008856922,0.0001509436,0.0006070565,0.0007907334,0.0008034328],"domain_scores_gemma":[0.9859865,0.01004645,0.0006189154,0.001590195,0.0003763518,0.001381536],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.0001658679,0.00006003987,0.00028236,0.00006617879,0.00001926417,0.0001900387,0.00553214,0.002197549,0.0002349181,0.9541569,0.01314591,0.02394881],"study_design_scores_gemma":[0.0000850015,0.00004157448,0.0001902893,0.0001621356,0.0000190518,0.00009121556,0.006508862,0.005309104,0.0003407931,0.881402,0.105821,0.00002888362],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.06273077,0.001889752,0.1069387,0.02552307,0.001184463,0.000149938,0.0001526894,0.0005375966,0.800893],"genre_scores_gemma":[0.9447309,0.000936542,0.015964,0.001990672,0.0002572694,0.0003085417,0.0001192962,0.0002412691,0.03545149],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.04712849,"threshold_uncertainty_score":0.1576605,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0188992360956224,"score_gpt":0.2653430849655363,"score_spread":0.2464438488699139,"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."}}