Infrastructure and its Discontents: Contestations of Power and Sociotechnical Imaginaries in Canada’s AI Governance Scaffold
Bibliographic record
Abstract
The following thesis develops a critical theoretical framework for understanding national artificial intelligence (AI) strategies as “Governance as Infrastructure” by integrating insights from Infrastructure Studies, Science and Technology Studies, with particular attention to Black Feminist Technoscience. To develop this framework, in-depth interviews were conducted with members of Canada’s technology policy community. Inductive thematic analysis revealed that AI governance infrastructure enacts specific configurations of power through the politics of expertise, that actors navigate profound ambiguities related to AI’s opacity, that mechanisms for justice and accountability struggle to address AI-specific harms, and that competing socio-technical imaginaries shape the contested development of this infrastructure. This thesis argues that by adopting a Black Feminist Technoscience perspective, we can understand the limitations of current governance approaches, while also identifying alternative infrastructural designs prioritizing care, maintenance, participatory democracy, and adaptive temporalities to foster more equitable and accountable AI futures.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.031 | 0.058 |
| Scholarly communication | 0.016 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".