Would Artificial Intelligence, Like ChatGPT, Be a Good ‘Peer’ Reviewer in Academic Publishing? A Human Versus AI-Based SWOT Assessment
Bibliographic record
Abstract
With the continuing passionate debate about the role of ChatGPT, an artificial intelligence (AI)–based text generator, in academic research and publishing, we reflect on whether AI can serve as a peer reviewer to overcome human weaknesses and other inherent weaknesses of the current human-supported peer-review model. The authors made their own assessment of this possibility by conducting a strength, weaknesses, opportunities, and threats (SWOT) analysis of human-based peer reviewers versus AI-based reviewers on 12 November 2023. A similar SWOT analysis was conducted for both humans and AI by employing cues fed to the paid version of ChatGPT (GPT-4). Despite the authors’ own ample experience and considerable efforts to make a balanced SWOT analysis, ChatGPT-4 was able to provide a comprehensive assessment, although it refused to cite relevant literature and obtained its information from websites and blogs. An earlier SWOT analysis (15 February 2023) using the free version (GPT-3) erred in citing the relevant literature while some citations and references were fabricated. Although AI (in this case, GPT-4) provided a logical and reasonable SWOT analysis, demonstrating its strength, it has not reached—in the authors’ view—a dependable stage yet to review articles for trusted academic journals.
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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 | 0.227 | 0.593 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.009 | 0.010 |
| Scholarly communication | 0.024 | 0.024 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".