Building the Legitimacy of Whistleblowers: A Multi‐Case Discourse Analysis
Bibliographic record
Abstract
ABSTRACT Evidence suggests that society still does not view whistleblowers as wholly legitimate—despite legal protections now offered in some jurisdictions, such as the United States. Drawing on a discourse analysis (i.e., an examination of statements), we investigate the well‐publicized stories of seven whistleblowers from 69 sources, including books, first‐ and second‐hand interviews, websites, and videos. Our focus is to examine how whistleblower discourses can build legitimacy by more tightly defining the whistleblower role and demonstrating its alignment with social norms. Using whistleblower self‐narratives, we identify four narrative patterns: (i) Trigger(s)—the event(s) leading to whistleblowing; (ii) Personality traits—whistleblower's morality, resourcefulness, and determination; (iii) Constraints—barriers requiring regulatory and organizational change; and (iv) Consequences—the longer term positive impact of the whistleblowing act. These patterns rely on symbolic, analogical, and metaphorical framing to allow others to better understand the role of whistleblowers and enlist their support. Exploring a data set of 1,621 press articles, we find indications that these narrative patterns resonate in the media—which provide a form of support and may be instrumental in legitimizing the whistleblower role. Grounded on these results, we develop a legitimacy construction model of the whistleblower role, that is, a representation of how role legitimacy is produced and sustained. From this model, we identify a number of important areas for future research.
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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 | 0.024 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.008 |
| Science and technology studies | 0.009 | 0.011 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".