Expectations and performance: assessment of public service training in Hong Kong
Bibliographic record
Abstract
There are different ways in which training providers and recipients assess the value and outcome of training programmes. Generally, evaluations by clients of training services in the public sector do not receive serious attention as one cohort of officials succeeds another. Such an approach restricts the prospect of improvement, particularly since the providers are not subjected to undergo self-assessment of their programmes. This article seeks to achieve a better understanding of the assessment by soliciting opinions of both clients and providers of training programmes offered by the Civil Service Training and Development Institute in Hong Kong. The views of both the trainers and recipients were collected through a number of surveys and interviews. The response from trainees and trainers reveal significant differences about the expectations and actual content of the training programmes. Interestingly, there were similarities as well in their assessment in some areas. A common position declared by the trainees is that training keeps them informed about the latest developments but does not help them to adjust to changing circumstances. The other complaint was that adequate training was not provided for performing on the job. Trainers expressed different views, but agreed on the fact that the institute is unable to cope with the task and responsibility of training the entire public service and conceded that it is difficult to anticipate the future training needs in the rapidly changing environment in which public administration takes place.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".