The Online Self-Disclosure of Workplace Mistreatment: A Novel Construct
Bibliographic record
Abstract
Anecdotal evidence suggests that employees who are mistreated at work are speaking out about these incidents online, despite the risks to both themselves (e.g., social retaliation, professional consequences) and their organizations (e.g., reputational damage). Throughout this paper, the construct of online self-disclosure is borrowed from the literatures of social, clinical, and media psychology, and applied to the context of speaking out about workplace mistreatment. Online self-disclosure of workplace mistreatment is differentiated from the established constructs of voice and silence, and expectancy theory and cognitive heuristics are integrated to suggest when a target may choose to disclose their experiences of workplace mistreatment online. Finally, propositions are presented to support future investigations into this unique behavioural response. Scholars and practitioners who are invested in eradicating workplace mistreatment are advised to pay attention to online self-disclosures, as these communications signal inadequate workplace voice mechanisms, and thus multiple workplace mistreatment-related problems that need to be addressed.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".