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
The latest wave of AI hype has been driven by ‘generative AI’ systems exemplified by ChatGPT, which was created by OpenAI’s ‘fine-tuning’ of a large language model (LLM). This process involves using human labor to provide feedback on generative outputs in order to bring these into greater ‘alignment’ with ‘safety’. This article analyzes the fine-tuning of generative AI as a process of social ordering, beginning with the encoding of cultural dispositions into LLMs, their containment and redirection into vectors of ‘safety’, and the subsequent challenge of these ‘guard rails’ by users. Fine-tuning becomes a means by which some social hierarchies are reproduced, reshaped, and flattened. By analyzing documentation provided by generative AI developers, I show how fine-tuning makes use of human judgement to reshape the algorithmic reproduction of inequality, while also arguing that the most important values driving AI alignment are commercial imperatives and aligning with political economy.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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