Performance Appraisal and Its Use for Individual and Organisational Improvement in the Civil Service of Ghana: The Case of Much Ado about Nothing?
Bibliographic record
Abstract
Recent efforts to modernise public sector institutions have led to the adoption of performance management systems worldwide. The belief in performance management is that information generated can be used to help individuals improve themselves in terms of what they do in their organisations, which will subsequently impact positively on the organisation. An instrument for collecting performance information (PI) is performance appraisal (PA). Since the early 1990s, the Ghanaian government has attempted to develop a systematic appraisal system as a strategy to obtain PI in the civil service (CS). In spite of this, the CS continues to perform below expectations despite individuals getting promoted every year. What has been the effect of PA in the CS? How has the collected information been utilised to improve performance? What are the main barriers to the use of PI, and what practices can be put in place that might encourage the effective collection of PI and its use in the CS? We argue that the PA system is much ado about nothing. In analysing why this is so, we will look at the impediments that continue to affect the collection and usage of PI and to suggest ways that will help improve the system. Copyright © 2015 John Wiley & Sons, Ltd.
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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 | 0.041 | 0.092 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.007 | 0.011 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.006 |
| 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".