Bibliographic record
Abstract
The notion of a toxic work environment was explored as a systemic organizational issue.Structural equation modeling of survey data from 501 participants revealed that workers' toxicity appraisals were associated with a variety of sources of workplace toxicity, including leaders, coworkers, and aspects of one' job and organization.Qualitative analyses of participants' open-ended comments depicted a range from very nontoxic work environments, wherein there was respect, constructive communication between management and employees, and issues were dealt with quickly, to very toxic work environments, wherein there was abuse, difficult conditions of work, and issues were left to fester.Although management-related sources of toxicity most strongly predicted toxicity appraisals, management's lack of involvement of workers in matters that affect them had a stronger influence on toxicity appraisals than did abusive supervision.The findings highlight the importance of taking a broad view of the workplace toxicity phenomenon, and suggest a need to shift the leadership focus in toxicity research from that of a 'toxic leader' to leadership that enables toxicity.To remedy a toxic work environment, interventions could involve changes to the ways in which management designs and monitors conditions of work, as well as how they react to workers' distress.Ultimately, given the complex dynamics involved, it is more effective to prevent workplace toxicity than to remediate it; recent trends in the promotion of workplace psychological health and safety provide direction for such prevention.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| 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".