Procurement Circuit under Machine Learning Political Order: \nGovernance of, through, and for AI
Bibliographic record
Abstract
In a landscape where governments are shaped by and depend on private AI providers, what does it mean to govern artificial intelligence (AI)? Public procurement is a point of intervention where the entrepreneurial pull of states to integrate digital expertise and reformulate its problem within machine-learning logics can be halted, questioned and examined. This thesis examines public procurement of AI as a crucial site where governments and AI providers engage in a complex co-shaping process, which I term the procurement circuit. Specifically, the thesis examines Canada’s procurement of AI as part of its national Responsible AI Strategy. \n \nThrough situational analysis, this thesis maps and explains how this co-shaping occurs and considers how the procurement circuit distributes authority and legitimacy over normative questions on AI between AI providers and government. I argue that Canada's regulatory architecture is built under what Louise Amoore coined Machine Learning (ML) political order. Chapter 3 maps the regulatory architecture Canada built to enforce Responsible AI and evaluate suppliers. Chapter 4 considers 11 suppliers’ responses to these requirements and outlines their normative views on both AI and its governance. In the conclusion, I suggest recommendations on how Canada might reformulate the procurement circuit as a space where legitimacy and authority is negotiated to resist ML political order.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.019 | 0.035 |
| Scholarly communication | 0.018 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".