Enhancing leadership capacity in the public sector: branding as an employer of choice
Bibliographic record
Abstract
Purpose Public services around the world are grappling to enhance their leadership capacity. One approach is to provide leadership training and development to public servants, while another would be to target and hire individuals with proven leadership ability. The purpose of this paper is to focus on that latter strategy by critically examining the concept of branding the public service as an employer of choice as a means of recruiting and retaining sufficient leadership capacity. Design/methodology/approach The paper begins by outlining the challenge faced by governments to recruit the best and brightest employees into the public service. It then reviews the literature on the concept of branding as an employer of choice, including its perceived advantages and limitations, before undertaking a case study analysis of the 2007 public sector branding initiative of the Canadian federal government. Findings It is very difficult to develop a single, master brand given the diversity and complexity of the hundreds of organizations that comprise the public sector. While it may be a popular concept for private sector organizations, the concept of branding the public sector as an employer of choice has yet to demonstrate its merit. Canada, which was ostensibly the vanguard in terms of public sector branding, has been unable to position the federal public service as an employer of choice. Originality/value There is very little research on the use of branding as an employer of choice in the public sector. This paper contributes to the knowledge about the limitations of such strategies for ensuring that the public sector has the requisite leadership capacity.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".