Bibliographic record
Abstract
Purpose Cyber incivility is a form of unsociable speech and a common daily workplace stressor. The purpose of this paper is to explore the impact of cyber incivility on non-profit leaders in Canada and share an intimate portrait of their personal experiences and perceptions. Design/methodology/approach The study advances our understanding of how qualitative methods can be introduced into the study of a phenomenon which has been broadly examined in a positivist tradition. The paper draws epistemologically and methodologically on a fusion of critical discourse analysis and auto-ethnography to present emic and experiential insights. Findings The findings offer three conceptual contributions: to introduce a novel qualitative method to a dynamic field of study; to advance a critical dimension to our understanding of cyber incivility; and to explore the challenges which emerge when qualitative research must draw largely on positivist, quantitative literature. Additionally, this paper makes three contributions to our understanding of cyber incivility: by introducing organizational context conditions which encourage incivility; by identifying commonalities between incivility and bullying, by challenging the existing taxonomy; and by examining the personal experiences of non-profit leaders in Canada (in operationalized settings). Originality/value Quantitative analysis has been limited to the relationship between supervisor and employee and consisted mostly of cross-sectional self-report designs, online surveys and experimental manipulation in simulated workplace environments. This study serves up a deeper analysis from within organizational environments.
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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".