Steering the governance of artificial intelligence: national strategies in perspective
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
ABSTRACT As more and more governments release national strategies on artificial intelligence (AI), their priorities and modes of governance become more clear. This study proposes the first comprehensive analysis of national approaches to AI from a hybrid governance perspective, reflecting on the dominant regulatory discourses and the (re)definition of the public-private ordering in the making. It analyses national strategies released between 2017 and 2019, uncovering the plural institutional logics at play and the public-private interaction in the design of AI governance, from the drafting stage to the creation of new oversight institutions. Using qualitative content analysis, the strategies of a dozen countries (as diverse as Canada and China) are explored to determine how a hybrid configuration is set in place. The findings show a predominance of ethics-oriented rather than rule-based systems and a strong preference for functional indetermination as deliberate properties of hybrid AI governance.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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