{"id":"W6976391391","doi":"10.60692/gbx1n-kmc61","title":"Assessing recall bias and measurement error in high-frequency social data collection for human-environment research","year":2019,"lang":"en","type":"article","venue":"Greater South Information System","topic":"ICT in Developing Communities","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Recall; Data collection; Recall bias; Task (project management); Precision and recall; Natural (archaeology); Quarter (Canadian coin)","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004875442,0.0001237257,0.0001889525,0.0004865613,0.0005049188,0.0008683588,0.0008746015,0.00009543125,0.000003199293],"category_scores_gemma":[0.00005185566,0.0001172962,0.00001798836,0.0003155734,0.0000384154,0.00277133,0.0006760282,0.0001780709,0.00008417796],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006737962,"about_ca_system_score_gemma":0.0001158114,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001075303,"about_ca_topic_score_gemma":0.000004896472,"domain_scores_codex":[0.9978519,0.0003550341,0.0005432593,0.0002092741,0.0007432919,0.000297233],"domain_scores_gemma":[0.9986728,0.00003838441,0.0002035698,0.0008135359,0.0002383778,0.00003332712],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"observational","study_design_scores_codex":[0.00007764684,0.00003294222,0.3913463,0.003687351,0.0001402353,0.000004010082,0.5600339,0.0002836695,0.0001263225,0.03697213,0.001583248,0.005712297],"study_design_scores_gemma":[0.005398703,0.0003177965,0.8133116,0.001554401,0.00002155584,0.00004056212,0.05982485,0.1152158,0.0007641097,0.000574482,0.001897001,0.001079117],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8317046,0.000005127497,0.1646067,0.0004755374,0.0004638162,0.001076852,0.00001535798,0.000123552,0.001528491],"genre_scores_gemma":[0.9895845,2.345268e-7,0.01014167,0.00003798793,0.00003530498,0.0001086245,0.00002661215,0.000007269265,0.0000577732],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.500209,"threshold_uncertainty_score":0.8373604,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4718406440827249,"score_gpt":0.352185229433347,"score_spread":0.1196554146493779,"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."}}