{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.07093404,0.0006515855,0.0009940241,0.004072288,0.01086105,0.03832366,0.003981324,0.008220731,0.01717095],"category_scores_gemma":[0.1807649,0.001375429,0.001666702,0.005303427,0.0264398,0.05353967,0.03082019,0.01133649,0.007398383],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.007970406,"about_ca_system_score_gemma":0.02585976,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008751277,"about_ca_topic_score_gemma":0.005368763,"domain_scores_codex":[0.9071232,0.05328087,0.005157823,0.007760111,0.02113996,0.005538145],"domain_scores_gemma":[0.7620739,0.1140289,0.01171046,0.07705057,0.0283252,0.006811032],"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":[0.00006084844,0.00006280789,0.001462013,0.0002313981,0.00002680961,0.0002198955,0.01356619,0.001021965,0.0008665278,0.8933837,0.04051181,0.04858595],"study_design_scores_gemma":[0.00003443027,0.00003323062,0.0007783575,0.0007098501,0.00003583324,0.0001479897,0.006840039,0.0009980501,0.002284118,0.3867213,0.6013491,0.00006757699],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.02648552,0.002819448,0.2916292,0.2720968,0.004867413,0.0008118507,0.002453105,0.003040301,0.3957964],"genre_scores_gemma":[0.702038,0.00529413,0.1575749,0.03895356,0.003832699,0.001865705,0.004140262,0.002907365,0.08339336],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.07093404,"threshold_uncertainty_score":0.3751395,"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."}}