Navigating Professional Dilemmas: How Public-Sector Engineers Navigate Ethical Tensions Arising from Conflicting Institutional Logics
Bibliographic record
Abstract
Over the last few decades, collegial forms of organization guided by norms of professionalism and shared decision-making have given way in public organizations to more corporate organizational forms that prioritize efficiency and economy. A growing body of research has explored these conflicting institutional logics, and identified the challenges of professional workers as they attempt to reconcile them on the job. At times, however, conflicting logics may create ethical dilemmas for professionals faced with competing imperatives, such as efficiency and public safety, if choosing the ethical imperative threatens their job security or professional standing. Their responses to such dilemmas have been under-explored in the literature. In this paper, we examine such dilemmas, and the responses to them, using qualitative data from public-sector engineers in two Canadian provinces. Public-sector engineers are ideal for such analysis because they work in changing environments where the tension between professional and managerial logics may be keenly felt. We find that these professionals have a range of responses, sometimes resisting and sometimes marginally acceding to workplace pressures. Light is thus shed on the circumstances under which ethical tensions might escalate.
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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 | 0.032 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.031 | 0.042 |
| Scholarly communication | 0.020 | 0.007 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".