Bibliographic record
Abstract
As two researchers faced with the prospect of still more knowledge mobilisation, and still more consultation, our manuscript critically reflects on strategies for engaging with consultations as critical questions in critical AI studies. Our intervention reflects on the often-ambivalent roles of researchers and ‘experts’ in the production, contestation, and transformation of consultations and the publicities therein concerning AI. Although ‘AI’ is increasingly becoming a marketing term, there are still substantive strategic efforts toward developing AI industries. These policy consultations do open opportunities for experts like the authors to contribute to public discourse and policy practice on AI. Regardless, in the process of negotiating and developing around these initiatives, a range of dominant publicities emerge, including inevitability and hype. We draw on our experiences contributing to AI policy-making processes in two Global North countries. Resurfacing long-standing critical questions about participation in policymaking, our manuscript reflects on the possibilities of critical scholarship faced with the uncertainty in the rhetoric of democracy and public engagement.
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 | 0.028 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.025 | 0.072 |
| Scholarly communication | 0.026 | 0.021 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.011 | 0.015 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".