Bibliographic record
Abstract
As I begin to write these comments, Canada and the entire world are consumed by the intense struggle to contain the COVID-19 pandemic.In February 2020, Canada's attention was focused on the blockades of rail lines by those supporting the Wet'suwet'en hereditary chiefs in their opposition to the Coastal GasLink pipeline in northern British Columbia, as well as focused on the Government of Canada's then-pending decision on whether or not to approve Teck Resources' application to build a very large oil sands project in northern Alberta.At that time, Warren Mabee, in an opinion piece posted on theconversation.com,wrote: "New energy projects have never faced such an uncertain pathway to success."In particular, his piece addressed the changing role of Indigenous peoples in making decisions about resource development in their territories.1 is book does the same, in a much more comprehensive way.It looks, in particular, at the experience many of its authors had in respect to the Keeyask Generating Project.It sets the context for this most recent development by reviewing the sixty-plus-year history of hydroelectric resource development in northern Manitoba.Manitoba Hydro's developments in northern Manitoba began with the Kelsey Generating Station on the Nelson River in the mid-1950s, built to support the nickel mine in ompson, followed by the Grand Rapids Generating Station on the Saskatchewan River in the 1960s.After that came the start of major development of the lower Nelson, which led to massive rearrangements of waterways throughout the province.ese included: the Churchill, Burntwood, and upper Nelson rivers; South Indian Lake; Playgreen Lake; and Lake Winnipeg, among many others.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.345 | 0.275 |
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".