Should We Collaborate with AI to Conduct Literature Reviews? Changing Epistemic Values in a Flattening World
Bibliographic record
Abstract
In this paper, we revisit the issue of collaboration with artificial intelligence (AI) to conduct literature reviews and discuss if this should be done and how it could be done. We also call for further reflection on the epistemic values at risk when using certain types of AI tools based on machine learning or generative AI at different stages of the review process, which often require the scope to be redefined and fundamentally follow an iterative process. Although AI tools accelerate search and screening tasks, particularly when there are vast amounts of literature involved, they may compromise quality, especially when it comes to transparency and explainability. Expert systems are less likely to have a negative impact on these tasks. In a broader context, any AI method should preserve researchers’ ability to critically select, analyze, and interpret the literature.
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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 | 0.717 | 0.811 |
| Meta-epidemiology (narrow) | 0.002 | 0.004 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.030 | 0.015 |
| Science and technology studies | 0.017 | 0.048 |
| Scholarly communication | 0.059 | 0.100 |
| Open science | 0.009 | 0.035 |
| Research integrity | 0.020 | 0.029 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".