{"id":"W3217026150","doi":"10.26443/glsars.v1i1.120","title":"Making Data Visible in Public Space","year":2021,"lang":"en","type":"article","venue":"McGill GLSA Research Series","topic":"Smart Cities and Technologies","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Nederlandse Organisatie voor Wetenschappelijk Onderzoek; Canadian Institute of Steel Construction","keywords":"Transparency (behavior); Open data; Data sharing; Internet privacy; Open government; Public space; Space (punctuation); Visibility; Government (linguistics); Public relations; Business; Computer science; Data science; Computer security; Political science; World Wide Web; Engineering; Geography","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005968365,0.000109013,0.0001577373,0.0002883761,0.0002000578,0.0001682611,0.0006651035,0.0001047528,0.0002103434],"category_scores_gemma":[0.0007816526,0.0001116083,0.00001882525,0.001188099,0.0001425724,0.000698533,0.001252594,0.0005121316,0.00006638108],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000111231,"about_ca_system_score_gemma":0.00004483478,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004694333,"about_ca_topic_score_gemma":0.001712772,"domain_scores_codex":[0.9984743,0.00006180389,0.0001535527,0.0002965556,0.000363657,0.000650118],"domain_scores_gemma":[0.9986504,0.0001315226,0.000007930525,0.001043968,0.0001211455,0.00004507638],"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":[0.00006054131,0.0001906798,0.01370703,0.001354311,0.0002324382,0.002646871,0.0005052061,0.001641686,0.02612189,0.7248957,0.1198629,0.1087807],"study_design_scores_gemma":[0.0001561515,0.00002750469,0.001497977,0.00007306065,0.000001205998,0.00001934857,0.003195358,0.002500134,0.01571651,0.004862097,0.9717759,0.0001747227],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.582059,0.008828238,0.0002959828,0.03018771,0.001314785,0.0006999732,0.0007924513,0.003766367,0.3720554],"genre_scores_gemma":[0.9956331,0.001181913,0.001914431,0.00002186575,0.00005045876,0.00003058881,0.00005075522,0.0000335206,0.001083364],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.851913,"threshold_uncertainty_score":0.4551253,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2696517455430231,"score_gpt":0.3776952783217802,"score_spread":0.1080435327787571,"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."}}