The Political Economy of Resource Extraction: Indigenous Peoples, Multinational Corporations and the State
Bibliographic record
Abstract
Transnational Governmentality in the Context of Resource Extraction S.Sawyer & E.T.Gomez On Indigenous Identity and a Language of Rights S.Sawyer & E.T.Gomez State, Capital, Multinational Institutions and Indigenous Peoples S.Sawyer & E.T.Gomez Indigenous Rights, Mining Corporations and the Australian State J.Altman Extracting Justice: Natural Gas, Indigenous Mobilization and the Bolivian State T.Perreault The Broker State and the 'Inevitability' of Progress: The Camisea Project and Indigenous Peoples in Peru P.Urteaga-Crovetto Development, Power and Identity Politics in the Philippines R.D.Rovillos & V.Tauli-Corpuz The Nigerian State, Multinational Oil Corporations and the Indigenous Communities of the Niger Delta B.Naanen Identity, Power and Development: The Kondhs in Orissa, India V.Xaxa Public-Private Partnership and Institutional Capture: The State, International Institutions and Indigenous Peoples in Chad and Cameroon K.Horta Identity, Power and Rights: The State, International Institutions and Indigenous Peoples in Canada M.Davis Attending to the Paradox: Public Governance and Inclusive International Platforms S.Sawyer & E.T.Gomez Appendix 1: International Conventions and IFI Policies on Indigenous Rights Appendix 2: Cross-Section of Domestic Legislation Pertaining to Indigenous Rights Appendix 3: Legal Institutions and Authorities for the Enforcement of Indigenous Rights
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.007 | 0.025 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".