{"id":"W4387161801","doi":"10.1080/01944363.2023.2253295","title":"Digital Technology Use and Future Expectations","year":2023,"lang":"en","type":"article","venue":"Journal of the American Planning Association","topic":"Land Use and Ecosystem Services","field":"Environmental Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Multinational corporation; Work (physics); Big data; Public relations; Emerging technologies; Business; Scenario planning; Knowledge management; Marketing; Political science; Computer science; Engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.01193898,0.0002810983,0.0003270915,0.002974453,0.00238282,0.009767434,0.001490005,0.001222965,0.006357483],"category_scores_gemma":[0.05326575,0.0003360909,0.0005208493,0.003982187,0.005975881,0.01133071,0.004590224,0.00306497,0.0005420568],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006469577,"about_ca_system_score_gemma":0.005956095,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00823373,"about_ca_topic_score_gemma":0.004322506,"domain_scores_codex":[0.9903473,0.004747781,0.0006727434,0.001062599,0.002156744,0.001012897],"domain_scores_gemma":[0.9580685,0.02603493,0.004244042,0.001464701,0.005751829,0.004435988],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002147389,0.0007298922,0.5784081,0.0006628101,0.00006075246,0.00093922,0.1303036,0.001251175,0.0002716118,0.2072434,0.006398608,0.07351611],"study_design_scores_gemma":[0.00003724669,0.0003259552,0.2934349,0.001277265,0.00007472615,0.001106521,0.5680786,0.005596367,0.0009620917,0.08053385,0.04844816,0.0001243383],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8688102,0.001595862,0.003568181,0.02905767,0.0001084989,0.0002758472,0.0008587671,0.00002738701,0.09569766],"genre_scores_gemma":[0.997329,0.0004070562,0.0004675088,0.0004460494,0.00001935646,0.0001312292,0.0001580468,0.000004864782,0.001036842],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01193898,"threshold_uncertainty_score":0.06314009,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007144325185430675,"score_gpt":0.2256118484951031,"score_spread":0.2184675233096724,"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."}}