HOW TO ASSESS ADMINISTRATIVE REFORM? INVESTIGATING THE ADOPTION AND PRELIMINARY IMPACTS OF THE NORWEGIAN WELFARE ADMINISTRATION REFORM
Bibliographic record
Abstract
This article addresses how to assess public-sector reforms using a reform in the Norwegian welfare administration as a case study. This reform represents a complex hybrid organizational form and a challenging combination of political control and local autonomy. We examine first how the reform has addressed its three main goals. These were to get people off welfare and back into work, to bring about more service-orientation, and to increase efficiency. We also address the side-effects of the reform by describing operational effects, process effects and system effects. Second, we examine how effects can be understood from an instrumental, cultural, and environmental perspective. A main finding is that context is significant for effects, and that it has so far proven difficult to discern clear overall effects concerning the main goals of the reforms and their side-effects.
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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 | 0.070 | 0.147 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".