Exploring a Model of Whistle Blowing System for Malaysian Municipal Council
Bibliographic record
Abstract
A municipal council is one of the local authorities established under the auspice of Malaysian local government. The objective is to deliver services to a community under its jurisdiction in a sustainable manner, but evidences indicated that the service qualities have deteriorated due to numerous malpractices. The malpractices could be prevented should any of the insiders who witness the incidences whistle blow to those who have power to act. However, not many are willing to report for fear of reprisal, retaliation and even life threatening. Despite that a whistleblowing system is established to encourage whistleblowing, such system has yet to be formed in any of Malaysian municipal councils. Intrigued with the issue, the objective of this study is to explore a model of whistle blowing system for a Malaysian municipal council. A hermeneutic phenomenography study was conducted in which data were gathered through in-depth interviews and focus group discussions. The participants consisted of four top management and sixty officials from a Malaysian municipal council. Data were analyzed qualitatively using Nvivo 14 and triangulated with other source of documents. The findings from emergent themes proposed a model of the whistleblowing system that consisted of four elements namely protection, internal control policies, incentives and ethical culture. This discovery provides useful insights to policy makers, relevant authorities, and academic fraternities of the model of whistle blowing system that will alleviate malpractices and thus elevate the transparency, efficiency and integrity of the municipal council to the fullest.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.011 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| 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".