Powerful or Just Plain Power-Full? A Power Analysis of Impact and Benefit Agreements in Canada’s North
Bibliographic record
Abstract
Impact and benefit agreements (IBAs) between natural resource developers and Aboriginal communities are increasingly portrayed as viable approaches to assure Aboriginal people will reap economic benefits of resource extraction in their traditional territories. Drawing from existing literature about the social context of IBA negotiations, especially in Northern Canada, the authors’ analysis contributes to the study of negotiated agreements by using Lukes’s three dimensions of power to examine how IBAs confer particular advantages and disadvantages to Aboriginal people and proponents of development, thereby distributing power inequitably. The authors argue that, under some conditions, IBAs may provide more direct engagement with industry and a sharing of benefits from resource development than heretofore has been provided in Northern Canada. Depending on the before-, during- and after processes and outcomes, IBAs can also stifle Aboriginal people from sharing information about benefits negotiated by other groups, prevent deeper understanding of long-term social impacts of development, thwart subsequent objections to the development and its impacts, and reduce visioning about the type and pace of development that is desirable.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.011 | 0.012 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".