{"id":"W2214725244","doi":"10.1016/j.geoforum.2015.11.017","title":"Between two hypes: Will “big data” help unravel blind spots in understanding the “global land rush?”","year":2015,"lang":"en","type":"article","venue":"Geoforum","topic":"Agriculture, Land Use, Rural Development","field":"Agricultural and Biological Sciences","cited_by":43,"is_retracted":false,"has_abstract":false,"ca_institutions":"International Development Research Centre","funders":"","keywords":"Sine qua non; Big data; Data science; Variety (cybernetics); Land grabbing; Space (punctuation); Value (mathematics); Business intelligence; Quality (philosophy); Computer science; Political science; Knowledge management; Geography; Epistemology; Law","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009473742,0.0002039513,0.0002222851,0.00001064705,0.0003357498,0.0001174941,0.000749319,0.0001101681,0.00003178513],"category_scores_gemma":[0.0000629299,0.00005799506,0.00004212604,0.0005689759,0.00006171993,0.0002396987,0.0008215467,0.0001690778,0.0001014718],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002107048,"about_ca_system_score_gemma":0.00003432921,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001042673,"about_ca_topic_score_gemma":0.00951468,"domain_scores_codex":[0.9979302,0.00007964759,0.0002697313,0.0003896185,0.0003934631,0.000937384],"domain_scores_gemma":[0.999419,0.0001159777,0.00009263775,0.000145369,0.00004049269,0.0001865519],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00005680022,0.00004570142,0.9563825,0.000003754101,0.0000315934,0.00001720927,0.0001816385,0.00002144074,0.00001641752,0.003906487,0.01033287,0.02900356],"study_design_scores_gemma":[0.0008104545,0.00007684381,0.8731458,0.0000375032,0.00001902819,0.00001520662,0.003239162,0.00003325661,0.00001814287,0.07087439,0.05142528,0.0003049155],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.990726,0.0002066303,0.000119906,0.004849411,0.0004289953,0.0003351213,0.000307994,0.00006161657,0.002964327],"genre_scores_gemma":[0.997879,0.00002465641,0.00005258121,0.0003827136,0.0007493109,0.00000763809,0.0006018456,0.000001188037,0.0003010359],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08323672,"threshold_uncertainty_score":0.5309415,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1954875698491254,"score_gpt":0.2814254332322866,"score_spread":0.0859378633831612,"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."}}