Reconfiguring Performance Information Linking with Accountability
Bibliographic record
Abstract
Over the past decade, performance information has been widely available to citizens along with the expansion of e-government, which has magnified communications between citizens and government as well as citizen direct participation in government business. If citizens are informed more about government performance, citizen trust in government should improve. However, there is, in effect, little use of performance information by citizens, since availability to citizens is not very visible. To disseminate the results of performance measurement effectively, government should pay attention to the improvement of performance measurement systems and performance reporting systems with citizen-centered approaches. User-friendly reporting should not just simplify the multi-layers of performance measurement for improving performance itself. Rather, this chapter suggests applying different approaches to present complicated performance information to citizens. Performance reporting should be constructed in modernized, innovative, and user-focused ways to stimulate the use of performance information by external stakeholders, which can promote government accountability.
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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 | 0.011 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.022 | 0.030 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.019 | 0.009 |
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".