Agency fever? Analysis of an international policy fashion
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
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 \ncase 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.
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 | 0.012 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.008 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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