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Record W2894412054

Free Prior and Informed Consent to mine development in the Yukon: Norms, Expectations, and the Role of Novel Governance Mechanisms

2018· dissertation· en· W2894412054 on OpenAlexaboutno aff
Emily Martin

Bibliographic record

VenueThe Atrium (University of Guelph) · 2018
Typedissertation
Languageen
FieldEngineering
TopicMining and Resource Management
Canadian institutionsnot available
Fundersnot available
KeywordsCorporate governanceInformed consentPolitical sciencePsychologyBusinessMedicineAlternative medicinePathologyFinance
DOInot available

Abstract

fetched live from OpenAlex

The adoption of the United Nations Declaration on the Rights of Indigenous Peoples (UNDRIP) has catalyzed Indigenous rights conversations in Canada around Free, Prior and Informed Consent (FPIC). The Yukon territory, where a majority of First Nations have self-government and settled land claims, provides a unique case for assessing how FPIC is being defined and exercised in light of possible mine developments. Findings from semi-structured interviews and document review revealed limited explicit engagement with FPIC by key Yukon governance institutions. This thesis serves to identify and make sense of this situation in an exploratory way, offering three factors: time, treaty implementation priorities, and awaited federal action, to explain the apparent lack of institutional engagement with FPIC. Despite instances of consent-like rights held by First Nations in the Yukon, there remains a lack of clear articulation from a majority of these First Nations about expectations for the meaningful expression of consent. Through a case study with the Little Salmon Carmacks First Nation (LSCFN), this research revealed that although LSCFN’s expectations of FPIC are not fully formed as of yet, they include: early and ongoing engagement, full and accessible information, internal engagement and governance processes, the mitigation of resource barriers, enforceable commitments, contextually relevant and mutually agreed upon processes, appropriate representation, agreed upon definitions, and the mitigation of power imbalances. Given the reticence of the State to acknowledge and implement FPIC this thesis also evaluates the treatment of FPIC by the Initiative for Responsible Mining Assurance (IRMA) standard, and largely confirms the comprehensiveness of that novel governance process relative to LSCFN’s emerging expectations around FPIC.

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.177
Threshold uncertainty score0.319

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 head0.009
GPT teacher head0.193
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 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

Citations2
Published2018
Admission routes1
Has abstractyes

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