States of computing: On government organization and artificial intelligence in Canada
Bibliographic record
Abstract
With technologies like machine learning and data analytics being deployed as privileged means to improve how contemporary bureaucracies work, many governments around the world have turned to artificial intelligence as a tool of statecraft. In that context, our paper uses Canada as a critical case to investigate the relationship between ideals of good government and good technology. We do so through not one, but two Trudeaus—celebrity Prime Minister Justin Trudeau (2015—…) and his equally famous father, former Prime Minister Pierre Elliott Trudeau (1968–1979, 1980–1984). Both shared a similar interest in new ideas and practices of both intelligent government and artificial intelligence. Influenced by Marshall McLuhan and his media theory, Pierre Elliott Trudeau deployed new communication technologies to restore centralized control in an otherwise decentralized state. Partly successful, he left his son with an informationally inclined political legacy, which decades later animated Justin Trudeau's own turn toward Big Data and artificial intelligence. Compared with one another, these two visions for both government and artificial intelligence illustrate the broader tensions between cybernetic and neoliberal approaches to government, which inform how new technologies are conceived of, and adopted, as political ones. As this article argues, Canada offers a paradigmatic case for how artificial intelligence is as much shaped by theories of government as by investments and innovations in computing research, which together delimit the contours of intelligence by defining which technical systems, people, and organizations come to be recognized as its privileged bearers.
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.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.029 | 0.014 |
| Scholarly communication | 0.013 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".