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Record W2904126112 · doi:10.4324/9781351311687-7

Powering Injustice: Hydroelectric Development in Northern Manitoba

2017· book-chapter· en· W2904126112 on OpenAlexaboutno aff
Steven M. Hoffman

Bibliographic record

VenueEnvironmental Justice · 2017
Typebook-chapter
Languageen
FieldSocial Sciences
TopicCanadian Identity and History
Canadian institutionsnot available
Fundersnot available
KeywordsHydroelectricityInjusticeGeographyEnvironmental planningEnvironmental ethicsPolitical scienceEngineeringPhilosophyElectrical engineeringLaw

Abstract

fetched live from OpenAlex

The pattern of ecological destruction and the dislocation of Aboriginal peoples in northern Canada caused by the Manitoba Hydro project make it an important case of environmental injustice deserving of greater attention. The Churchill River Diversion and the Lake Winnipeg Regulation projects allowed Manitoba Hydro to develop the Nelson River as a "power corridor" and to turn Lake Winnipeg into a gigantic "storage battery". Hydroelectric development was perhaps the single most important vehicle for the perpetuation of this history in the last decades of the 20th century. Critics of the Cross Lake position argue that the community cannot expect Manitoba Hydro or the governments of Manitoba and Canada to restore a now-extinct way of life. The impacts of hydroelectric resources in northern Manitoba extend far beyond the traditional lands of the Crees and, indeed, far beyond the boundaries of Manitoba. The development of Manitoba&s;s northern resources offers a case of transnational environmental injustice breathtaking in its scope.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.057
Threshold uncertainty score0.414

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0210.008
Scholarly communication0.0040.001
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.011
GPT teacher head0.209
Teacher spread0.197 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2017
Admission routes1
Has abstractyes

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