Bibliographic record
Abstract
Beginning with the Grand Rapids Dam in the 1960s, hydroelectric development has dramatically altered the social, political, and physical landscape of northern Manitoba. The Nelson River has been cut up into segments and fractured by a string of dams, for which the Churchill River had to be diverted and new inflow points from Lake Winnipeg created to manage their capacity. Historic mighty rapids have shrivelled into dry river beds. Manitoba Hydro's Keeyask dam and generating station will expand the existing network of 15 dams and 13,800 km of transmission lines. In Our Backyard tells the story of the Keeyask dam and accompanying development on the Nelson River from the perspective of Indigenous peoples, academics, scientists, and regulators. It builds on the rich environmental and economic evaluations documented in the Clean Environment Commission’s public hearings on Keeyask in 2012. It amplifies Indigenous voices that environmental assessment and regulatory processes have often failed to incorporate and provides a basis for ongoing decision-making and scholarship relating to Keeyask and resource development more generally. It considers cumulative, regional, and strategic impact assessments; Indigenous worldviews and laws within the regulatory and decision-making process; the economics of development; models for monitoring and management; consideration of affected species; and cultural and social impacts. With a provincial and federal regulatory regime that is struggling with important questions around the balance between development and sustainability, and in light of the inherent rights of Indigenous people to land, livelihoods, and self-determination, In Our Backyard offers critical reflections that highlight the need for purposeful dialogue, principled decision making, and a better legacy of northern development in the future.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.004 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.294 | 0.120 |
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".