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Record W7001243514

 Indigenous Peoples’ Perspectives on Participation in Mining The Case of James Bay Cree First Nation in Canada

2009· other· en· W7001243514 on OpenAlexaboutno aff

Bibliographic record

VenueKTH Publication Database DiVA (KTH Royal Institute of Technology) · 2009
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousFirst nationBayIndigenous rightsMining industryLand rightsScale (ratio)
DOInot available

Abstract

fetched live from OpenAlex

Mining exploration and production are rapidly increasing in remote regions of the world where traditionally large scale mining has not taken place such as in the North of Quebec in Canada. In these remote areas, mining companies frequently take over lands and territories of Indigenous Peoples disrupting their traditional livelihoods. Indigenous Peoples have specific rights to land and resources, rights to free prior informed consent as well as participation in decision making. A number of CSR initiatives have been taken by mining companies to shift towards responsible business and participation of Indigenous communities in decision making. Yet the implementation of meaningful approaches to participation is not common or in many cases not properly applied in practice. Furthermore although Aborginal particpation is highly promoted in the business industry little is known how Indigenous communities perceive proper conditions for participation and FPIC process. This study examines the perspectives of James Bay Cree First Nations in the North of Québec on the participation process with Troilus mine project and the implementation and implications of the Troilus agreement on the Cree. Additionaly the study scrutinizes the internal participation and FPIC process in two Cree communities and the impacts of mining on the Cree First Nation. Key words: Indigenous Peoples, mining, livelihood, human rights, participation, FPIC, Cree First Nation, CSR, corporate Aboriginal agreement, development impacts, Canada.

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)0.001
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.186
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0040.004
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.024
GPT teacher head0.273
Teacher spread0.249 · 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.

Study designNot applicable
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
Published2009
Admission routes1
Has abstractyes

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Same venueKTH Publication Database DiVA (KTH Royal Institute of Technology)French-language works237,207