Bibliographic record
Abstract
For this Choux Questionnaire, we turned to ChatGPT, the generative AI chatbot. Given the challenges and opportunities that AI presents to academic practice, teaching, and writing, we thought it might be intriguing to use these responses as a means to interpret ChatGPT’s ‘perspectives’ on food through our own. Both the process and outcomes of conducting the questionnaire provided occasions to reflect on the underlying technology, its sources of ‘knowledge’, and its apparent biases. In reading the bot’s words below, a fairly distinct character profile might emerge, as well as a kind of positionality that seems connected to both no place and every place at once. Beyond social and physical geographies, a set of privileges also tends to emerge, one that points to a lack of actual, lived experience. Where are the preferences, quirks, and affect that non-artificial intelligence comprises? Where are the outlier and emotional responses that would make one want to share food or ideas with this being? From your perspective as food scholar, practitioner, eater, or activist, what else do you extrapolate from ChatGPT’s ‘voice’?
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 | 0.005 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.126 | 0.037 |
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".