Performance improvement, culture, and regimes
Bibliographic record
Abstract
Purpose – The purpose of this paper is to better understand the performance improvement outcomes that result from the interaction of a performance regime and its context over more than a decade. Design/methodology/approach – A series of partial free disposable hull analyses are performed to graph variations in performance for 13 services in 444 municipalities in one province for over a decade. Findings – There are few examples of mass service improvements over time. This holds even for relative bottom performers, as they do not catch up to average municipalities over time. However, there is also little proof of service deterioration during the same period. Research limitations/implications – A limitation results from the high churning rate of the indicators. The relevance of refining indicators based on feedback from practitioners should not be dismissed, even if it makes the task of proving performance improvement more difficult. It is possible that the overall quality of services on the ground improved, or stayed stable despite diminishing costs, without stable indicators to capture that reality. Practical implications – Not all arrangements incentives and structures of – performance regimes – are equally fruitful for one level of government to steer a multitude of other governments on the generalized path to improved performance. Social implications – With the insight that was not available to public managers putting together these performance regimes in the beginning of the 2000s, the authors offer a proposition: mass performance improvement is not to be expected out of intelligence regime. It neither levels nor improves performance for all (Knutsson et al., 2012). Though there are benefits to such a regime, a general rise in performance across all participants is not one of them. Originality/value – Performance improvements are assessed under difficult, yet common characteristics in the public sector: budgetary realities where there are trade-offs between many services, locally set priorities, no clear definition of what constitutes a good level of performance, and changes in the indicators over time.
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 | 0.018 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.005 | 0.019 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| 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".