Workshop 3: Training and Developing Leaders Is Public Sector Leadership Distinct? A Comparative Analysis of Core Competencies in the Senior Executive
Bibliographic record
Abstract
Following the lead of the private sector, which widely embraced the use of competency-based management in the 1990s, many governments have created their own competency models for recruiting, assessing, developing and compensating their most senior public service employees. Using a comparative case study approach (Canadian, Ontario, American and Australian Public Services), this paper will critically analyze the efficacy of using core competency models as an integral component of public sector leadership development strategies. The paper argues that effective leadership competency frameworks must be sufficiently parsimonious that they can be actually be meaningfully utilized in hiring, compensation and promotion decisions but suitably nuanced so that they recognize that leading in the public sector, while in many respects similar, is not completely analogous to leading in the private sector. In doing so, it examines the extent to which the competency models developed in these four cases effectively capture the essence of leading in the public sector by clearly establishing a distinctive public sector leadership brand. 1
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".