Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The United Nations Declaration on the Rights of Indigenous Peoples (UNDRIP) offers guidance on how the rights of indigenous populations could be protected in the context of member states of the United Nations. While the Declaration prescribes what states need to do to effectively realize its objective, question is whether there are expectations on non-state actors such as corporations to contribute towards attaining those objectives. Though on the one hand the UNDRIP is textually not directed at corporations, on the other hand, corporations are routinely implicated in environments where massive violations of indigenous rights have occurred in various regions of the world. The main argument of this paper is that whereas the UNDRIP does not specifically mention corporations, the contributions of businesses would nonetheless be essential for the effective implementation of UNDRIP in Canada. In the paper, I intend to examine how the text of the indigenous policies of Canadian corporations align with objectives of the UNDRIP. I do so by analyzing a representative sample of indigenous human rights policies of Canadian corporations to see the extent that they engage with the UNDRIP and whether their policies could facilitate best-practice ideas for UNDRIP implementation. The sample policies will be assessed for their substantive content, normative language, potential weaknesses, and possible impact on UNDRIP implementation in the Canadian context. In particular, I will pay close attention to whether the studied policies have enough ingredients to meaningfully contribute to the achievement of UNDRIP goals in Canada as well as indicate any possible impacts they could have on broader corporations/indigenous communities’ relations.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it