Should Artificial Intelligent Agents be Your Co-author? Arguments in Favour, Informed by ChatGPT
Bibliographic record
Abstract
Academics have long relied on technological tools to support their research, with these tools growing in sophistication over time. As these tools have advanced, they have allowed researchers to create knowledge more effectively than could have been undertaken by humans alone. However, this paper argues that some new technologies may be moving from simple tools to being collaborators in research, with their abilities contributing not only to identifying previously unidentified relationships in the data, but also synthesising and explaining information to external audiences. Relying on existing literature and questions posed to ChatGPT, we argue that artificial intelligence tools have, or will have, the ability to meet the four conditions specified in the International Committee of Medical Journal Editors (ICMJE) recommendations for authorship (the Vancouver Protocol), warranting these technologies to become co-authors on the advancement of academic endeavours; not just background support.
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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 | 0.135 | 0.513 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.011 | 0.029 |
| Scholarly communication | 0.022 | 0.037 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.035 | 0.026 |
| Insufficient payload (model declined to judge) | 0.018 | 0.007 |
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".