{"id":"W3143954530","doi":"10.4314/dai.v21i3.48216","title":"Justice in the Forest: Rural Livelihood and Forest Law Enforcement","year":2009,"lang":"en","type":"article","venue":"Discovery and Innovation","topic":"Conservation, Biodiversity, and Resource Management","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Livelihood; Deforestation (computer science); Law enforcement; Harm; Illegal logging; Government (linguistics); Business; Enforcement; Economic Justice; Natural resource economics; Environmental planning; Environmental protection; Logging; Political science; Agriculture; Geography; Forestry; Law; Economics","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":[0.00380935,0.0001586842,0.0002907482,0.001171572,0.007801112,0.006928316,0.001032338,0.003588583,0.0105354],"category_scores_gemma":[0.006845402,0.0001368256,0.0001732119,0.002093618,0.02415353,0.00544215,0.004753564,0.003299054,0.0003595056],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006628178,"about_ca_system_score_gemma":0.00750334,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01956562,"about_ca_topic_score_gemma":0.03722592,"domain_scores_codex":[0.9962319,0.001972688,0.00007458172,0.0002478614,0.0004136058,0.00105935],"domain_scores_gemma":[0.993064,0.003927497,0.001217683,0.0002216238,0.0005337888,0.001035402],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"qualitative","study_design_scores_codex":[0.00002430842,0.0001873036,0.01181292,0.0002457996,0.00001344949,0.0006162993,0.02436067,0.0003426255,0.0001451085,0.8788664,0.01977634,0.06360881],"study_design_scores_gemma":[0.00002952281,0.0001277871,0.04731135,0.001741358,0.00002138928,0.0007928195,0.1357229,0.001031189,0.0002979093,0.5227944,0.2900737,0.00005573502],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.185033,0.04230339,0.004072978,0.2579921,0.0006074461,0.00008026976,0.00006948326,0.0000242239,0.5098172],"genre_scores_gemma":[0.9723485,0.008620335,0.0003226992,0.00663399,0.0002585363,0.00002692433,0.00001709554,0.000006097448,0.01176601],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01956562,"threshold_uncertainty_score":0.04809099,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01702976261321051,"score_gpt":0.2267679013085635,"score_spread":0.209738138695353,"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."}}