Shallow Depths: Reproductions of Nuclear Landscapes and Anomalies of Consent at Chalk River
Bibliographic record
Abstract
Scholars and Indigenous communities increasingly urge courts and regulators to uphold the principle of “free, prior, and informed consent” (FPIC) as articulated in the United Nations Declaration on the Rights of Indigenous Peoples. In Canada, the Nuclear Waste Management Organization (NWMO) has ostensibly embraced FPIC through its “willing host model,” designed to identify potential sites for a deep geological repository (DGR) to house radioactive waste. In this doctoral research, I examined the siting process for a Near Surface Disposal Facility (NSDF) in Chalk River, Ontario. I attended public hearings and community meetings as part of close empirical work documenting opposition to the proposed NSDF. In this dissertation, I demonstrate how opposition, and Indigenous refusal in particular, is generative in terms of political and regulatory shifts toward asserting and advancing Indigenous rights. The research raises the question of why the willing host model is appropriate for the DGR, but not the NSDF. I argue that consent, in this framework, is used a tool to manage and generate legitimacy in order to secure a bare minimum social license to operate. In practice, the willing host model exhibits the hallmarks of a “divide and conquer” strategy, with people in neighbouring communities being pitted against each other as they weigh the very uncertain costs and benefits of nuclear waste storage proposals. My research demonstrates that a major barrier to workable consent processes is the significant time and resources required to implement them. Effective frameworks must account for the complex and differentiated geographies of risk and impact, the intergenerational effects, and the cumulative social and financial costs, including the exacerbation of intra- and inter-community divisions. This dissertation disentangles tensions of the NWMO's willing host model, with the ultimate vision of 'scaling up' consent-processes to broader extractive contexts. This research advances critical human geography and environmental justice literatures by teasing out how spatial and local political-economic-social contexts shape both decision-making and its outcomes—specifically, how these contexts influence the operationalization of consent-based policies, the mechanisms through which consent is negotiated, and the extent to which the nuclear waste siting process is deliberative, fair, and just.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".