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
Generative AI is a uniquely public technology. The large language models behind ChatGPT and other tools that generate text and images is a major develop in publicity as much as technology. Without public data and public participation, these large models could not be trained. Without the attention, hype, and hope around these technologies, the big AI firms probably could not afford the computational costs to train these models. Our special issue questions how Critical AI Studies can attend to the publics, publicities, and publicizations of generative AI. We situate AI’s publicity as mode of publicity – hype, scandals, silences, and inevitability – as well as a mode of participation seen in the grown importance of technology demonstrations. Within this situation our contributions offer four different research paths: (1) situating the legacy media as an enduring process of legitimation; (2) looking at the ways that AI has a private life in public; (3) questioning the post-democratic future of public participation; and, (4) developing new prototypes of public participation through research creation.
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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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