Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it