{"id":"W7043280662","doi":"","title":"Science, technology, engineering, and mathematics workforce crisis or science, technology, engineering, and mathematics workforce surplus","year":2014,"lang":"en","type":"dissertation","venue":"DSpace@MIT (Massachusetts Institute of Technology)","topic":"Diverse Education and Engineering Focus","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; National Institutes of Health; Massachusetts Institute of Technology","keywords":"Workforce; Workforce planning; Workforce development; Government (linguistics); Public policy","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0006154745,0.0001814498,0.000228357,0.0007667859,0.0009624651,0.001603689,0.0002886546,0.001088204,0.0205671],"category_scores_gemma":[0.003463232,0.0000537391,0.0001750006,0.0008181608,0.001332183,0.001802806,0.00109458,0.001113126,0.00122785],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001016623,"about_ca_system_score_gemma":0.002214798,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001806679,"about_ca_topic_score_gemma":0.003553838,"domain_scores_codex":[0.9997508,0.00005802524,0.0000161869,0.00005169029,0.00004867277,0.00007460294],"domain_scores_gemma":[0.9990596,0.0002999356,0.0002355964,0.00003110458,0.0001350121,0.0002387152],"domain_codex":null,"domain_gemma":"incentives","domain_candidate":"incentives","domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002060906,0.0003329217,0.02395887,0.000561649,0.00002108208,0.0004444942,0.009610849,0.0002716896,0.000942193,0.533998,0.3236134,0.1060388],"study_design_scores_gemma":[0.00008695421,0.0001634319,0.1056319,0.003170352,0.00006915512,0.001048745,0.06398851,0.0009484905,0.001586474,0.251047,0.5722089,0.00005015424],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.1913119,0.01744325,0.001830171,0.1433819,0.002005924,0.0001157211,0.002510322,0.00006109767,0.6413396],"genre_scores_gemma":[0.9274567,0.01612533,0.0006144931,0.009518872,0.001275244,0.00007879099,0.0008844988,0.00003509341,0.04401103],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9993845,"threshold_uncertainty_score":0.06880379,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01445315314716971,"score_gpt":0.2824079980097547,"score_spread":0.267954844862585,"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."}}