Self-Worth and Identity: The Influence of Workplace Violence and Harassment in Canadian Workplaces
Bibliographic record
Abstract
As innately social beings, individuals crave acceptance and yearn to find a sense of purpose in life. More often than not, this sense of purpose is linked to careers or roles within a system. Examination of individuals’ perception of self-worth following acts of violence in the workplace is limited. The gap in the literature on self-worth following experiences of violence and harassment is critical to explore because not only is self-worth linked to overall well-being, but individuals also vary in outcomes following the survival of violence (Breines et al., 2008; Sojo et al., 2016). Violence within the workplace is evident within all systems and becomes increasingly difficult to navigate as power, intersectionality, and organizational structure intertwine (Sojo et al., 2016; Gunnarsson, 2018). Each of these pieces plays a role in how individuals conceptualize their identity and their beliefs around their self-worth. This project aimed to identify how workplace violence impacts self-worth, including how identity factors contribute to the perception of self-worth and violence. Results demonstrated that harassment directed toward intersectional aspects of identity was comprehended as an attack on the entire group of individuals who share that identity factor and contributed to feelings of ostracization. Additionally, survivors of workplace harassment and violence reported negative outcomes on their self-worth and overall mental well-being. Overall, participants perceived harassment in the workplace as necessary to ignore in order to avoid additional conflict and consequences.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.017 | 0.004 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 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".