Bibliographic record
Abstract
Purpose This paper aims to explore how well reporting mechanisms work, investigate current trends and develop a framework for implementing effective mechanisms. Design/methodology/approach This study is based on primary and secondary data, criminology theory and best corporate strategies. Findings This study shows that the median number of annual reports equals 1.2 per cent of the number of employees in an organization and that 40 per cent of these reports have merit (Navex Global, 2014). In addition, 42.2 per cent of all frauds are detected through internal reports, whatever their form. Organizations with formal reporting mechanisms sustain fraud losses that are 40.5 per cent less than other organizations (ACFE, 2014). Moreover, employees are more willing to report theft, human resource and workplace issues than fraud and corruption, while 21 per cent of all whistleblowers have experienced some form of retaliation for reporting wrongdoing (Ethics Resource Center, 2014). Results from primary data show that the option to remain anonymous is offered only by 74 per cent of all reporting mechanisms. This paper argues that effective reporting mechanisms should actively encourage whistleblowing, that all credible allegations should be independently investigated and that whistleblowers should be offered the option to remain anonymous. The oversight and the daily administration of reporting mechanisms should be given to two different parties who are independent from management and who do not participate in incentive compensation plans (Lipman, 2012). Research limitations/implications This paper extends previous research by reporting on current hotline trends and integrating various factors into a framework to implement effective reporting mechanisms. Originality/value It is the first paper to investigate the effectiveness of reporting mechanisms and current policy trends.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.280 | 0.568 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.005 | 0.016 |
| Scholarly communication | 0.030 | 0.043 |
| Open science | 0.008 | 0.009 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".