Workplace and Policy Ethics: A Call to End the Solitudes
Bibliographic record
Abstract
Judging from a perusal of the media, there appear to be two clusters of ethical issues before Canadian society. On the one hand are the stories of workplace corruption, fraud, influence peddling, cronyism, and so on. On the other hand are the stories on vexing ethical questions relating to policies on human rights, animal welfare, genetic engineering, relations with developing nations, just war, etc. The workplace integrity issues would seem to be clearly distinct from ethical issues in social, environmental or economic policy, but is the ethics applied to each type of issue really unique? Many people will feel uneasy about the idea that ethics is used one way here, and another way elsewhere. This brief examines the level of present divergence, the arguments for greater convergence, and the governance options to achieve increased convergence. Our examination will focus on the example of the Government of Canada, but much will also be applicable to other governments, the voluntary and private sectors. We will refer from time to time to the role of political actors, but we do not address here the special problems of political ethics such as “dirty hands”, party financing, and political patronage.
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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 | 0.054 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.023 | 0.123 |
| Scholarly communication | 0.032 | 0.042 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.036 | 0.047 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".