Teaching the âRightâ Thing: Wrestling with Ethics Instruction in Non-Profit Management Studies
Bibliographic record
Abstract
Non-profit organizations (NPOs) enjoy more trust than most other modern institutions. That trust comes at a price, however. When allegations of unethical behaviour arise, public indignation is swift and can translate into decreased donations for several years. NPOs typically serve vulnerable populations and, consequently, individuals working in them are expected to put organizational and public interests above their own. Yet, increased pressure to perform efficiently to compete for decreasing government funds may lead to compromised values. As the sector grows and increases in importance as the “third sector” between government and the market[1], high-profile scandals worldwide suggest a need for ethics training for non-profit administrators.\nBoth government and the public have called for greater accountability in the non-profit sector, creating the current trend toward professionalism and a growing number of professional study programs. But is the post-secondary, non-profit administration program the best place for future professionals to learn what constitutes “right”, or ethical, behaviour? Do ethics courses actually make a difference in the ethical decision-making process and the behaviour of practicing professionals? If so, how are they currently being taught and how should we teach these types of courses? This workshop examines these challenges within the context of a currently “fragmented” state of ethics pedagogy in non-profit management studies. \n[1] As described by, for example: Brock, K. L. (2000). Sustaining a relationship: Insights from Canada on linking the government and the third sector. Working Paper 1, School of Policy Studies, Queen’s University, Ontario. Retrieved from www.queensu.ca/sps/publications/workingpapers/01.pdf
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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 | 0.022 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.011 |
| Scholarly communication | 0.014 | 0.016 |
| Open science | 0.003 | 0.023 |
| Research integrity | 0.008 | 0.014 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".