Infrastructural (Dis)Entitlement: Tactics of Dispossession on the Critical Minerals Frontier
Bibliographic record
Abstract
In Ontario’s far north, settler state authorities and extractive firms are engaged in coordinated tactics to gain ground amid a polarization in the positions of Indigenous leadership. Alongside a surging resistance, we also witness a resigned acceptance of critical minerals mining by some First Nations. Drawing on years of community-engaged research, I detail here the contemporary tactics of “infrastructural (dis)entitlement:” in this dynamic, infrastructural needs are both denied and fulfilled to differential effect. Infrastructural disentitlement is passive; it is not necessarily deliberate, nor is it politically or institutionally organized. But infrastructural entitlement is strategic and aggressive: Indigenous prosperity and inclusion are key elements of the contemporary liberal justification for critical minerals extraction. From this, a pattern emerges of places toward which resources are flowing and places out of which they are draining. The chronic lack of community-focused infrastructure in some remote First Nations—characterized as a form of “letting die”—creates an attritional force that undermines the communities’ capacity to defend their homelands, to the advantage of the settler state and extractive firms.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.048 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".