Bibliographic record
Abstract
Mounting concerns about the potential for unethical uses, the incorporation of bias, and the risks associated with an unregulated artificial intelligence (AI) environment have led to growing support for common policies, principles, and regulation. Achieving consensus on both the governance of AI and the substance of potential policies or principles remains elusive, however. Many countries have introduced AI strategies and policies, but their approach often differs, ranging from market-led, self-regulated models to government-led initiatives featuring intensive market intervention. This chapter brings the challenge of a global AI regulatory consensus into sharp relief by surveying approaches found around the world. It begins with a review of the Canadian approach to date. While Canada has been actively engaged in AI policy development and demonstrated a clear commitment to prioritizing both the economic opportunities offered by AI and the need for an appropriate forward-looking policy response, the Canadian AI policy model remains at best a work-in-progress. The chapter continues by examining the three most notable approaches: the less prescriptive, market-led approach in the United States, the government-led system in the People’s Republic of China, and the hybrid approach that seeks to combine regulation and self-regulatory principles in the European Union. The chapter represents a spotlight of policy initiatives at a moment in time, but the trends are unmistakable, pointing to a broad spectrum of approaches that will be difficult to reconcile if the goal is to develop binding, enforceable rules that extend beyond high-level principles with little legal weight.
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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.008 | 0.023 |
| Scholarly communication | 0.015 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.027 | 0.003 |
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".