Bibliographic record
Abstract
This paper examines how AI tools that excel at interpolating existing knowledge affect scientific research directions.Using a model where AI assists both scientists (S-AI) and decision-makers (DM-AI), it is shown that AI's impact on research novelty is non-monotonic and depends critically on capability thresholds.While S-AI predictably encourages knowledge consolidation by reducing costs within established domains-potentially creating distinct pockets of deepening-DM-AI generates surprising effects.With limited capabilities, scientists ignore DM-AI.In a moderate regime, scientists "work to the AI," constraining novelty to match AI's operational range.Only with sufficiently advanced DM-AI do scientists unambiguously pursue more novel research.The strong complementarity between AI capabilities and knowledge gaps means that moderate AI may reduce research ambition.These findings challenge the conventional wisdom that interpolative AI uniformly pushes science toward consolidation, revealing a nuanced relationship between AI capabilities and scientific progress instead.
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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 | 0.018 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.026 |
| Scholarly communication | 0.013 | 0.028 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.008 | 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; 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".