Incentivising, excluding, and enduring: Insular policy feedback in Lithuanian research assessment
Bibliographic record
Abstract
Performance-based funding systems (PBFSs) are widely used to steer national research, but their effects vary significantly, particularly in countries with emerging research ecosystems. Relatively little attention has been paid to PBFSs and their concomitant policy dynamics in these countries, where the pressure to internationalise creates unique challenges. This paper presents a detailed study of the development of the Lithuanian PBFS from 2005 to 2022. Using a multi-level, multi-actor, and multi-issue framework, we combine policy analysis, semi-structured interviews, and bibliometric data to analyse the system’s evolution. Our findings reveal a dynamic of “insular policy feedback,” where a concentrated scientific elite, operating across all levels of governance, shapes policy to its advantage. This results in predictable cycles of strategic gaming, such as the proliferation of domestic journals, followed by reactive and often inconsistent state countermeasures. The Lithuanian case serves as a model for understanding how concentrated power structures can undermine reform, offering a crucial insight for policymakers: meaningful reform must address the governance structures that empower performance metrics, not just the metrics themselves.
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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 | 0.036 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.013 | 0.005 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".