Bibliographic record
Abstract
Purpose The purpose of this paper is to explore how the competing values framework (CVF) could be used by public service leaders to analyze and better understand public sector leadership challenges, thereby improving their ability in leading across borders and generations. Design/methodology/approach This paper applies the CVF, originally developed for understanding leadership in the private sector and shows how it can be adapted for analyzing and developing skill in addressing different leadership challenges in public sector contexts, including setting out specific learning exercises. Findings The paper has four parts. The first provides an overview of the origins, logic, and evolution of the CVF. The second part shows how the CVF is relevant and useful for assessing management and leadership values in the public sector. The third part identifies specific leadership challenges and learning exercises for public sector leaders at different stages of development. The final part concludes by reflecting on the CVF and similar frameworks, and where future research might go. Research limitations/implications Because of the chosen research approach, propositions within the paper should be tentatively applied. Practical implications This paper provides guidance for the better understanding of complex leadership challenges within the public sector through the use of the CVF. Social implications The social implications of the paper could include the more widespread use of the CVF within the public sector as a tool to lead more effectively. Originality/value This paper adapts and extends an analytical tool that has been of high value in the private sector so that it can be used in the public sector.
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 | 0.019 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.007 | 0.028 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".