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Record W7033695022

Realizing Reconciliation: Analyzing Resource Revenue Sharing Agreements and Indigenous-Settler Relations in Ontario's Mining Industry

2025· other· en· W7033695022 on OpenAlexaboutno aff

Bibliographic record

VenueYork University Digital Library (York University) · 2025
Typeother
Languageen
FieldEngineering
TopicMilitary Technology and Strategies
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousState (computer science)Resource cursePoliticsRevenueGovernment (linguistics)EmbeddednessRevenue sharingPrinciple of legality
DOInot available

Abstract

fetched live from OpenAlex

In 2018, the Government of Ontario and the Grand Council Treaty #3, the Mushkegowuk Council and the Wabun Tribal Council announced that they had entered into three resource revenue sharing agreements. These new agreements were publicly celebrated as an example of reconciliation with First Nations in Ontario. This dissertation critically studies the development of these new agreements, from their conceptual origins in reconciliation to how they reshape Indigenous-settler relations in Ontario. Relying on settler colonial studies, governmentality, and Mills’ legality tree as theoretical frameworks, and through archival research, textual analysis and the principles of Indigenous research methodologies, this dissertation paints a complex picture of reconciliation, resource revenue sharing and settler colonialism in Ontario. I begin by theorizing the lifeworld of liberalism, noting how Christian creationism influenced liberal philosophy, settler colonialism, and reconciliation discourses. I argue that Christian theology is anthropocentric, which is reflected by how state actors use reconciliation discourses, and that this contrasts with Indigenous worldviews. Despite the narrower conception of reconciliation, I contend that reconciliation, as a political rationality, was heavily influenced by Indigenous peoples seeking to remake their relationship with the state in the 1990s. I explicate that resource revenue sharing emerged as a new governmental concept for repairing Indigenous-settler relations. Next, I trace the development of reconciliation as a new political rationality in Ontario. I then examine the decade and a half long story of how resource revenue sharing went from a divisive concept to one that was widely accepted by a diverse assemblage of Indigenous, state, extractive and market actors in Ontario, and how it was developed into a reconciliatory technology. With humility, I also consider how these new agreements can affect Indigenous-settler relations in Ontario. I reason that while reconciliation produces concessions for Indigenous peoples from settlers, it may still leave in place asymmetrical power relations, as settlers seek to govern Indigenous peoples in accordance with their own settler colonial interests regarding resource extraction. However, rather than concluding that reconciliation is an ideology that only shields settler colonial relations, I argue that since reconciliation has a concessionary logic, reconciliation discourses have the potential to affect the actions of settler actors, even if it does not end settler colonialism. This is further explored by how the Progressive Conservative government eschewed reconciliation to de-Indigenize resource revenue sharing, and how there are fierce discursive struggles to define reconciliation and influence the scope of its concessions. Finally, I assert that reconciliation produces messy alliances, technologies and outcomes, and that future scholarship requires a careful and contextual analysis of reconciliation.

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)0.003
metaresearch head score (Gemma)0.008
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: Empirical
Teacher disagreement score0.083
Threshold uncertainty score0.600

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.006
Science and technology studies0.0150.012
Scholarly communication0.0070.003
Open science0.0010.005
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.010
GPT teacher head0.156
Teacher spread0.146 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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