Extracting Intelligence and (In)security: Corporate-state Information-sharing Practices and the Construction of Narratives of Indigenous Dissent
Bibliographic record
Abstract
This thesis aims to examine how narratives of Indigenous land defense movements are constituted by investigating natural resource extraction corporations and Canadian intelligence and national security agencies joint participation in information-sharing practices. I consider a specific set of “information-sharing practices” otherwise understood as acts or activities set out as part of state national security strategies. I argue that in these practices, state and corporate interests converge due to their shared investment in upholding settler colonial authority. While points of divergence remain, these practices legitimize the classification of dissent as a national security concern and further surveillance of Indigenous land defense movements in the “national interest.” I conducted a thematic discourse analysis on a mixed dataset of institutional records, primarily retrieved from Access To Information (ATI) requests, to map the narratives advanced within them. Additionally, I conducted a comparative content analysis to identify the prevalence of threats across three different national security involved actors. I show that corporate-state information-sharing practices advance a narrative that emphasizes economic harm as the primary risk of dissent against natural resource extraction projects. Further I argue that another narrative advanced suggests an ever-present suspicion of dissent, with Indigenous dissent discursively constructed as eminently at risk of escalating. I conclude that these narratives have spilled over into legal and political discourse, resulting in the criminalization and polarization of Indigenous dissent.
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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 | 0.015 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.018 | 0.048 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.001 | 0.012 |
| 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".