Transnational Social Justice and the Forest Stewardship Council’s Endorsement of Free, Prior and Informed Consent
Bibliographic record
Abstract
This chapter analyzes the adoption, development and promotion of the emerging customary international legal norm of indigenous peoples’ free, prior and informed consent (FPIC) by the Forest Stewardship Council (FSC). This case reveals how transnational business governance interactions can offer an avenue to develop, import and apply elements of the powerful international discourse of human rights inside states, with the potential of strengthening domestic standards and advancing marginalized actors. It also offers insight into the influential role that can be played in these interactions by a class of non‐state actor often neglected in accounts of transnational governance: the transnational indigenous peoples’ movement. Indigenous representatives and their allies leveraged FSC’s inclusive governance structures, the moral force of international human rights law, and interactions between FSC International and FSC Canada to strengthen the FPIC norm in FSC standards. This has led to the FSC’s advocacy of a strengthened and enriched version of FPIC that compares favorably with the most robust readings of FPIC by international organizations and states, situates FSC Canada at the leading edge of indigenous rights norm development in transnational governance, and opens an avenue toward a de facto requirement for ongoing indigenous consent to forestry operations in Canada. [Note: Title revised to "Transnational Social Justice and the Forest Stewardship Council’s Endorsement of Free, Prior and Informed Consent" on 2019-10-23]
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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 | 0.059 | 0.068 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.021 | 0.070 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.017 | 0.019 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".