Agency fever? Analysis of an international policy fashion
Bibliographic record
Abstract
In the last 15 years, the governments of many OECD countries have transferred a wide range of functions to new, agency‐type organizations. Allowing for the fact that, for comparative purposes, it is difficult precisely to define agencies, and further acknowledging that in many countries agencies are far from being new, it nevertheless remains the case that there seems to have been a strong fashion for this particular organizational solution. This article investigates the apparent international convergence towards “agencification.” It seeks to identify the reasons for, and depth of, the trend. It asks to what extent practice has followed rhetoric. The emerging picture is a complex one. On the one hand, there seems to be a widespread belief, derived from a variety of theoretical traditions, that agencification can unleash performance improvements. On the other hand, systematic evidence for some of the hypothetical benefits is very patchy. Furthermore, the diversity of actual practice in different countries has been so great that there must sometimes be considerable doubt as to whether the basic requirements for successful performance management are being met.
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 | 0.017 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.009 | 0.023 |
| Scholarly communication | 0.024 | 0.019 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.018 | 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".