Removing Barriers to Whistleblowing at Nonprofit Organizations through Employee Empowerment*
Bibliographic record
Abstract
Abstract This study explores how key organizational and governance actors perceive the effectiveness of whistleblowing at nonprofit organizations (NPOs) and how whistleblowing is interrelated with other anti‐fraud mechanisms. Using a systems approach, we develop a conceptual framework of anti‐fraud mechanisms consisting of a set of interrelated components: control‐focused mechanisms and employee‐focused mechanisms (whistleblowing) intended to prevent and detect fraud, influenced by the environment (regulation and stakeholders) and human factors (employees' attitudes and leaders' awareness of fraud). We conducted 14 semistructured interviews with key actors at Canadian NPOs and noted that diverse control mechanisms were in place at these groups, but seemingly no formal whistleblowing policy existed. The organizations were disinclined to formalize a whistleblowing system in the short term despite viewing such a system as effective. Whereas prior research has examined the role of control‐focused mechanisms, NPOs' adoption of whistleblowing systems, and the benefits thereof, we contribute to the literature by stressing that employee empowerment is crucial to overcoming reluctance to blow the whistle. If the board of directors is aware of fraud risk and provides employees with the resources, motivation, and protection to speak up, whistleblowing could be implemented in these organizations. Whistleblowing should be interrelated with other mechanisms to form an effective anti‐fraud system.
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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 | 0.013 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".