“It’s the Lack of Structure that’s Causing the Problem”: Managerial Competence, the Treatment of Workers, and Job Stress in Precarious Firms
Bibliographic record
Abstract
In this study, we use Hodson’s concept of Management Citizenship Behavior (MCB) and a case study research design of 16 small Canadian information technology (IT) firms to examine the interrelationship between insecure work environments, management behavior, and job stress within the context of the organizations. Within the study firms, the presence of MCB in the form of competent and respectful management was associated with a positive work environment and less job stress. The relationship between insecurity and stressful work environments was less straightforward and could only be understood in combination with MCB. Findings suggest that management behavior may moderate the relationship between precarious employment and stress, particularly within the context of small firms in a sector that is an important exemplar of work in the new economy. Competent and respectful management practices may alleviate the stress associated with job insecurity within precarious firms, and in contrast, their absence may create a pervasive culture of stress even within stable firms. Results indicate the importance of studying the organizational context established by the actions of owners and managers and suggest that good management can create healthier work environments even within the context of otherwise harmful job conditions.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".