FD専門職団体による倫理規定の基礎調査 : PODとSEDAを事例として
Bibliographic record
Abstract
This paper investigates the role and content of ethical principles and codes of conduct of professional organizations for faculty development with particular focus on the Staff and Educational Development Association in the United Kingdom (SEDA), and the Professional and Organizational Development Network in the United States (POD). Professionalization of staff in faculty development or its equivalent such as educational development and academic development is taking place in many countries like the United States, the United Kingdom, Canada, and Australia. In these countries, there are professional organizations to support practitioners and promotes faculty development. The position of faculty development practitioners is considered as the ‘Third Space Professionals’ (Whitchurch, 2008), which are characterized as having loose boundaries with their responsibilities and shifting professional identities. Then how do the professional organizations such as SEDA and POD support its members with their professional development? According to the studies on professions, it is argued that ethical principles and codes of conduct are critical for professional organizations to constitute and revise when the profession itself undergoes transformation (Nishimura, 2006). Thus, this study examines ethical principles and codes of conduct of SEDA and POD and discusses the following questions: 1) How were they constituted and why? and 2) How is it shared among members?
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".