Administrative leaders as institutional entrepreneurs in developing countries: A study of the development and institutionalization of performance management in Ghana's public service
Bibliographic record
Abstract
Summary Performance management (PM) has become one of the most important reforms in the public sector in both developed and developing countries. Unfortunately, institutionalizing PM in the sector continues to be a major problem. Although a number of scholars continue to study the PM in the public sector from different theoretical perspectives, there has been paucity of research concerning the process of institutionalization. The few studies that have attempted to look at the process have done so from a “coercive isomorphism” perspective, especially through legislation. The lack of studies to examine the process of institutionalization has therefore created a gap, which needs to be filled. We therefore attempt to contribute to this discussion by exploring the role of institutional entrepreneurs and their impact on the development and institutionalization of PM in developing countries, with a particular focus on Ghana, a country obsessed with PM but was only able to institutionalize one in 2013 under the leadership of its Public Services Commission. How was the leadership of the PSC able to succeed where previous leaders could not? What characteristics did they display, and what strategies did they use to get things done? The research is a qualitative one.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".