The Strong Focus on Output Information: A Threat to Evaluation in the Swedish State Sector?
Bibliographic record
Abstract
Abstract: This article discusses the issue of combining different kinds of results information for decision-making in the public sector. Its purpose is to take part in the debate on public management and management by results, by arguing that the trend in public management is a focus on information about outputs and a decreasing interest in information about outcomes. Since the mid-1980s there has been an obvious drop in the interest in results information in the public sector in Sweden and in many other countries (those within the OECD for instance). The favoured concept for the state sector in Sweden is management by results. Ministries and agencies have developed skills to measure not only costs, but also quality and performance. It is easy to believe that this development also should stimulate the development of evaluations of outcomes and in-depth analyses, as these evaluations are important to the concept of management by results. However, we argue that simpler and more focused follow-up information has increased and so also has interest in such information. In short, interest in information about outputs has increased at the expense of interest in information about outcomes. This threatens to diminish the future role of evaluation.
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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 | 0.260 | 0.431 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.007 | 0.015 |
| Scholarly communication | 0.028 | 0.017 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 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".