Bibliographic record
Abstract
This paper argues that, despite a very large volume of management reform in the EU over the past quarter century or more, our knowledge of the impacts of these efforts is patchy and fragile. It calls on a number of key studies which each, in a different way, carries this same basic message. In the second part of the paper the reasons for this state of affairs are analyzed. One set of reasons is concerned with the considerable methodological difficulties of evaluating major reforms, which are commonly complex, evolving and spread out over time. A second set of reasons concerns the limited incentives for politicians and top officials to take a sustained interest in evaluation. A concluding reflection is that, while this basic situation is not likely to change, there are nevertheless ways in which the academic contribution to reform evaluation can be strengthened.
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.
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 | 0.195 | 0.356 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.004 | 0.023 |
| Scholarly communication | 0.032 | 0.026 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".