{"id":"W2963261759","doi":"10.48550/arxiv.1804.02318","title":"How Constraints Affect Content: The Case of Twitter's Switch from 140 to 280 Characters","year":2018,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Digital Marketing and Social Media","field":"Social Sciences","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Constraint (computer-aided design); Computer science; Social media; Limiting; Affect (linguistics); Quality (philosophy); Character (mathematics); Time constraint; Content (measure theory); Upper and lower bounds; Information retrieval; World Wide Web; Mathematics; Psychology; Engineering; Political science","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":[0.0004839655,0.0001073069,0.0001695969,0.00005630895,0.0004065509,0.00009902133,0.0003590519,0.00008373684,0.00007764567],"category_scores_gemma":[0.0006583216,0.00009488467,0.00009940331,0.0004308193,0.001232576,0.0002281928,0.00007756212,0.000099981,0.00005760699],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007386886,"about_ca_system_score_gemma":0.00008510613,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00450181,"about_ca_topic_score_gemma":0.003535005,"domain_scores_codex":[0.9989917,0.0002723126,0.0000863827,0.0002774854,0.00008327588,0.0002888321],"domain_scores_gemma":[0.9987555,0.0005424973,0.0001065895,0.0002238175,0.0001722431,0.0001993763],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"qualitative","study_design_scores_codex":[0.001212102,0.0006931571,0.1489431,0.00006456608,0.0009705034,0.008529392,0.2280186,0.0000208078,0.005428527,0.5145289,0.00713296,0.08445734],"study_design_scores_gemma":[0.00490194,0.001268364,0.09063909,0.000623688,0.0006548074,0.00005522818,0.8272209,0.000489185,0.004408488,0.02746848,0.03991711,0.002352693],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.975287,0.000005403526,0.0003856684,0.001223486,0.0004901966,0.0002301505,0.00002733301,0.00004973571,0.02230099],"genre_scores_gemma":[0.996815,0.00001027063,0.00001493906,0.0002571432,0.0002790838,4.817912e-7,0.000001980027,0.000007681595,0.002613468],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5992023,"threshold_uncertainty_score":0.680542,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1128971759894889,"score_gpt":0.2224812009398462,"score_spread":0.1095840249503573,"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."}}