The Integrity of Local Enforcement Officers: Self Proclaim vs Colleague Perception
Bibliographic record
Abstract
Social development requires the removal of barriers so that all citizens can live with confidence and dignity. This development is driven with integrity, to sustain society trust and wellbeing with the help of local enforcement officers. However, local enforcement integrity issues become more serious since they serve public interest and constantly deals with law offenders and public criminals. This study therefore, examines the effects of demographic, experience and organisational factors on the integrity of local enforcement officers. The data was collected in 2017 from Pusat Latihan Penguatkuasa Selangor, a training centre for local enforcement officers in Malaysia. Based on vignettes survey of 216 respondents, a paired-samples t-test analysis was carried out. The results indicate that there is a significant difference between self-proclaim and colleague perception only in the ‘willingness to report’ and not ‘offence seriousness’ category. This study helps local authorities, government, private organisation and the policy makers to ensure integrity issues can be prevented thoroughly in all areas.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".