{"id":"W2521108914","doi":"10.1111/joes.12199","title":"LOCAL LABOR MARKETS AND NATURAL RESOURCES: A SYNTHESIS OF THE LITERATURE","year":2017,"lang":"en","type":"article","venue":"Journal of Economic Surveys","topic":"Mining and Resource Management","field":"Engineering","cited_by":101,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Economics; Earnings; Natural resource; Labor demand; Labour economics; Population; Spillover effect; Poverty; Educational attainment; Personnel economics; Distribution (mathematics); Labor relations; Wage; Economic growth; Microeconomics; Sociology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004170593,0.0009275808,0.002667184,0.009661375,0.0004758869,0.003090977,0.0009795976,0.001112447,0.006378473],"category_scores_gemma":[0.01635528,0.0006165307,0.002158207,0.01355254,0.0008141773,0.001960438,0.001308297,0.001059102,0.0003984651],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002942063,"about_ca_system_score_gemma":0.004567063,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008280939,"about_ca_topic_score_gemma":0.01341395,"domain_scores_codex":[0.9979414,0.000892547,0.0004892938,0.0002818413,0.0003153876,0.00007949682],"domain_scores_gemma":[0.9849605,0.01279028,0.001169941,0.0001728887,0.0007915839,0.0001147695],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"systematic_review","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000267907,0.0001174425,0.005121289,0.6416178,0.005639506,0.0002631189,0.001677863,0.001220996,0.0002137961,0.009765808,0.01074524,0.3233493],"study_design_scores_gemma":[0.00007327127,0.0001851586,0.02302305,0.8067625,0.01345372,0.0004591656,0.002820564,0.0004682401,0.0002034715,0.008134664,0.1443495,0.00006661889],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.001340427,0.9967247,0.0002181492,0.0006490785,0.00009267353,0.00003111842,0.0002103913,0.000003173912,0.0007304261],"genre_scores_gemma":[0.01471539,0.9840319,0.0004442901,0.0003583173,0.00009979496,0.00008444623,0.0001562513,0.000002838175,0.0001068021],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.009661375,"threshold_uncertainty_score":0.02205646,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007084159316866482,"score_gpt":0.1997976791181785,"score_spread":0.192713519801312,"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."}}