Responsible but Innovative Use of Artificial Intelligence in Scientific Publishing
Bibliographic record
Abstract
In late November 2022, what is arguably the first widely accessible large language model (LLM) ChatGPT (chat.openai.com), was released for public use and appraisal. This represents a step on the road towards artificial intelligence by statistically mimicking human generated factual and stylistic content; using large mathematical models which have been trained on web sites, in books, and other media. and further more curated and corrected by humans working with the OpenAI team. There have been many enthusiastically proposed use cases in the medical field and the release of preliminary pilot products, such as “AI medical scribes,” and equally, a number of concerns raised involving accuracy, security, privacy, and ownership.
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 | 0.091 | 0.177 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.008 | 0.052 |
| Scholarly communication | 0.041 | 0.046 |
| Open science | 0.005 | 0.021 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.026 | 0.014 |
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".