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Record W4392815881 · doi:10.29173/jaed318

Alternative Approaches To Hydro Compensation And Agreements With First Nations: Manitoba and Quebec

2012· article· en· W4392815881 on OpenAlexafffundabout
John Loxley

Bibliographic record

VenueJournal of Aboriginal Economic Development · 2012
Typearticle
Languageen
FieldEngineering
TopicMining and Resource Management
Canadian institutionsUniversity of Manitoba
FundersSocial Sciences and Humanities Research Council of CanadaManitoba Hydro
KeywordsNegotiationHuman settlementEquity (law)PoliticsRevenueIntergenerational equityCompensation (psychology)Settlement (finance)Political scienceBusinessEconomicsLawGeographyFinanceSustainability

Abstract

fetched live from OpenAlex

This paper outlines the workings of two quite different contemporary approaches to settlements and agreements with First Nations by hydro companies and governments involved in hydro dam construction. The first is the equity approach used by Manitoba Hydro in negotiations with the Nisichawayasikh Cree Nation (NCN) in which the First nation is effectively offered joint ownership of the dam and a share in future income streams and in employment and construction benefits. The second approach is that by the James Bay Cree of northern Quebec who eschew dam ownership, instead negotiating an annual share in revenues generated by hydro, forestry and mining. Both approaches constitute major improvements over disastrous earlier approaches which can be summarized as 'flood now and talk later', but they carry quite different economic. Political and governmental terms as well as quite different potential benefits and risks. This paper examines the background behind each deal and the way in which they operate. It concludes by arguing that each deal was conditioned by circumstances and history. There is, however, clear merit in Aboriginal People seeking to secure maximum control over and benefit from all sources of economic development on their traditional lands.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.593
Threshold uncertainty score0.342

Codex and Gemma teacher scores by category

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

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.

Opus teacher head0.027
GPT teacher head0.210
Teacher spread0.184 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations0
Published2012
Admission routes3
Has abstractyes

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