Mapping Artificial Intelligence Use in the Government of Canada
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
On the one hand, technological advances and their enthusiastic uptake by government entities are seen as a push toward a Canadian dystopic state, with friendly bureaucrats being replaced by impassive machines. On the other hand, embracing technology is considered a confident move of the Canadian administrative state toward an utopian low-cost, high-impact decision making process. I will suggest in this paper that the truth—for the moment, at least—lies somewhere between the extremes of dystopia and utopia. In the federal public administration, technology is being deployed in a variety of areas, but rarely, if ever, displacing human decision making. Indeed, technology tends to be leveraged in areas of public policy that don’t involve any settling of benefits, statuses, licenses, and so on. We are still a long way from sophisticated machine learning tools deciding whether marriages are genuine, whether taxpayers are compliant or whether nuclear facilities are safe. The reality is more down to earth. In this paper, I map out the uses of algorithms and machine learning in the federal public administration in Canada. I will briefly explain my methodology in Part I; in Part II, I identify seven different use cases, which I describe with the aid of representative examples, and offer some critical reflections.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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