Bibliographic record
Abstract
O'Reilly (private citizen) prepared a call for research on independent oversight bodies and experiences to support the environmental assessment of the Giant Mine Remediation project. In response to this request, two research projects were initiated. Natasha Affolder, from the Faculty of Law, University of British Columbia is undertaking a comparative analysis of the legal and institutional aspects of environmental oversight agencies (see Appendix A). Patricia Fitzpatrick, from the Department of Geography, University of Winnipeg is considering the role of oversight bodies in project implementation, community involvement, research, enforcement and monitoring (see Appendix B). It is our intention to prepare written reports on our findings, which the three parties will submit to the Mackenzie Valley Environmental Impact Review Board. The purpose of this letter is twofold. First, we wanted to inform the Board of our research programmes. Second, we request that, should the Board revise its work plan and timelines (given the delayed submission of the developer’s report), consideration be given to our timelines. We believe our research can make a significant contribution to the Environmental Assessment, is relevant to section 3.6 of the Terms of Reference
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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.010 |
| Insufficient payload (model declined to judge) | 0.042 | 0.019 |
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".