Buy Canadian: policy options for localizing federal public procurement
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Globally and domestically, procurement localism is on the rise. This can take the form of a Buy Canada policy that favors local suppliers. Assuming the localism of a Buy Canada is preferable, how can federal procurement policies and guidelines be designed to maximize its local returns? This article profiles three potential policy design challenges that may undermine a federal Buy Canada policy’s implementation and evaluation. First, Canadian international trade commitments ensure nondiscrimination, putting many contracts out of the reach. Second, simplistic approaches to capturing a supplier’s origin, like a given address, would result in marginal change. Third, data gaps like subcontracting diminish policy renewal and enable leakages to unknown foreign suppliers. Using Buy America as a counter case, this article makes recommendations to address these design gaps and maximize Buy Canada. It suggests new federal trade derogations, employee-defined origin, domestic sourcing requirements and subcontracting data collection.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it