Understanding Public Sector Ethics: Beyond Agency Theory in Canada's Sponsorship Scandal
Bibliographic record
Abstract
Public sector ethics is a topic of ongoing concern in developed democracies. The most popular theoretical approach to this issue is found in principal–agent theory literature. This approach assumes that public sector organizations are populated by principals and agents, each of whom pursue their own self-interest, with agents having a persistent informational advantage. A second approach to ethical conflicts focuses on cognitive processes. According to cognitive theory, all decision makers are vulnerable to “ethical numbing,” particularly in organizational settings that condone the substitution of personal agendas for organizational goals. We argue that Canada's sponsorship scandal has been interpreted almost exclusively from a principal–agent perspective, with subsequent reforms firmly based on introducing new rules to oblige agents to advance the interests of principals. While more faithful adherence to established rules by agents would have avoided a scandal, such adherence is unlikely to be achieved through incentives, monitoring, and penalties as suggested by principal–agent theory. The policy message contained in and implied by the cognitive framework suggests that the focus must be on creating an organizational learning environment that discourages responsible public officials from reframing decision situations in a manner that allows them to become morally disengaged.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.024 | 0.042 |
| Scholarly communication | 0.019 | 0.007 |
| Open science | 0.002 | 0.004 |
| 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".