Investigating FPIC: Can Peace-Culture Complement the Absence of Meaningful Consent? : An analysis of Indigenous Rights and Resource Extraction in Canada and Sweden
Bibliographic record
Abstract
Despite the increase of policies, guidelines, and developments in international law, the actual recognition of Indigenous peoples’ rights remains at odds in the collaborative management of Indigenous territories. Numerous studies demonstrate that mining companies have been slow to adopt international legal developments, particularly regarding Free, Prior, and Informed Consent (FPIC). States and natural resource companies often fail to adequately consult with affected Indigenous communities and rarely seek their consent before exploiting natural resources. Sweden and Canada have, despite making generalized claims about ethical behavior, respect for human rights and recognition of historical injustices, legislations that promote resource companies’ to extractivism. The purpose of this study is to examine the interpretation and implementation of FPIC in a Swedish and Canadian context, using a comparative qualitative content analysis, based on purposive sampling. In order to investigate conflicts between the Indigenous communities, local non-communities, the state itself, and commercial mining interests in Nunavut (Canada) and Laponia (Sweden), we aim to explore what interpretations and implementations of FPIC that exist between stakeholders and what mechanisms that are used for advocating interests. By doing this, we compare the contexts with focus on how corporate policies, practices and state narratives frequently diverge from FPIC principles. The study explores the possibility of integrating the concept of ‘the culture of Peace’ or ‘Peace-Culture’ with FPIC, which emphasizes peaceful approaches to conflict resolution. The themes are presented as ‘Indigenous knowledge’, ‘Asymmetric Power relations and Triangular conflict’, as well as ‘Persisting Post-Colonial Structures’. The study indicates that both Canada and Sweden lack effective mechanisms for obtaining consent from Indigenous communities and that the conflicts emerge from a combination of structural, cultural, and extractive violence. We further propose that fostering a Peace-Culture approach could enhance the implementation of Free, Prior, and Informed Consent (FPIC).
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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 | 0.014 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.047 | 0.045 |
| Scholarly communication | 0.015 | 0.004 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".