Beyond the Berger Inquiry: Can Extractive Resource Development Help the Sustainability of Canada’s Arctic Communities?
Bibliographic record
Abstract
The four decades since the Berger Inquiry have produced a large body of research demonstrating the positive and negative impacts of resource development on northern communities. However, little independent research has aimed to yield an understanding of how best to manage the impacts of resource development and to harness its benefits in ways that can promote long-term sustainable development. This question was the impetus for the Resources and Sustainable Development in the Arctic (ReSDA) research project funded by the Social Sciences and Humanities Research Council of Canada in 2011. Representing a network of researchers, community members and organizations, ReSDA researchers conducted a series of analyses that focused on what was needed to ensure that northern communities received more benefits from resource development and potential negative impacts were mitigated. Overall, the analyses highlight the serious gaps that remain in our ability to ensure that resource development projects improve the sustainability of Arctic communities. These gaps include a proper understanding of cumulative impacts, the ability of communities to adequately participate in new regulatory processes, the non-economic aspects of well-being, the effects of impact and benefit agreements and new financial benefits, and new mitigation activities.
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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.026 | 0.021 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".