Unpacking the state: an agential constructivist assessment of Natural Resources Canada’s implementation of the UNDRIP
Bibliographic record
Abstract
Though an emerging literature has sought to assess the progress made by Canada in terms of United Nations Declaration on the Rights of Indigenous Peoples (UNDRIP) recognition and implementation, these studies tend to treat national governments as unitary state actors. Such approaches tend to include only a cursory reference to the government agencies and departments that convert calls for action into actual policy. Employing content analysis and an agential constructivist approach, this article remedies this oversight by assessing the extent to which Natural Resources Canada (NRCan) Departmental Plans bring Canada’s federal government into alignment with UNDRIP. The article’s contribution is four-fold. First, it depicts how greater insights can be obtained by unpacking the strategies and decision-making of state actors. Second, it illustrates the interplay between international “soft law” (e.g. UNDRIP) and national “hard law” (e.g. legislation). Third, the article shows how differing conceptions of what constitutes “Indigenous Knowledge” and “consultation” influence UNDRIP recognition and implementation in practice. Fourth, it finds that the contemporary dynamics of Canada’s foreign policy and national interests pertaining to natural resource governance demonstrate that neither federal Departments nor Indigenous Peoples are passive actors in the creation, maintenance, and redefinition of transnational norms.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.017 | 0.030 |
| Scholarly communication | 0.016 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".