A comparative account of indigenous participation in extractive projects: The challenge of achieving Free, Prior, and Informed Consent
Bibliographic record
Abstract
Indigenous peoples’ right to Free, Prior, and Informed Consent (FPIC) has been recognised as an important principle to ensure their meaningful participation in decision-making processes related to extractive projects. Yet, many companies grapple with their duty to engage in good faith consultations with indigenous peoples implied by the standard of human rights due diligence. Instead of understanding project impacts from the perspective of these peoples, companies generally conduct one-off environmental or social impact assessments or sign private agreements with communities that look at project impacts merely in terms of the reputational, operational, legal, and financial costs they represent to them. Human Rights Impact Assessments recognise that human rights conditions evolve and that companies need to consult with affected rights-holders throughout the project cycle to renew community consent on a regular basis. Indigenous peoples are also taking matters into their own hands by conducting Community-Controlled Impact Assessments or community consultations to move consent-based processes to the centre of negotiations with companies. By comparing local experiences of corporate-indigenous engagement in Canada, Guatemala, and Peru, we aim to determine if and how companies currently contribute to the implementation of FPIC in order to suggest a way forward towards greater corporate commitment to FPIC.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.016 | 0.020 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| 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".