Bibliographic record
Abstract
Purpose The purpose of this paper is to introduce, illustrate and explore the use of process recording as a pedagogical tool in behavioural ethics education. Design/methodology/approach An overview of the nature and components of process recording as a pedagogical tool is provided. Potential challenges and benefits associated with its use are described. The particular relevance of process recording for behavioural ethics education is highlighted. Illustrative examples of ethics-related process records are discussed. Findings Process recording shows promise as a pedagogical technique for meeting three goals of behavioural ethics education (i.e. Chugh and Kern, 2016). These include: enhancing literacy with research-supported concepts and principles such that these can be applied in “real-world” settings; increasing student awareness of gaps that might exist between their intended and actual ethical behaviour; and, fostering the sense that ethical skills are not static, but rather, open to development. Research limitations/implications This paper introduces, illustrates and explores the use of process recording in behavioural ethics education. Additional, more systematic study of process recording in ethics education would be useful. Practical implications Process recording shows promise as a tool for supporting learning about behavioural ethics. Practical information on its use and concrete examples are provided. Originality/value Despite the need for pedagogical tools in behavioural ethics education, as well as the previously identified relevance of process recording as a potential tool in ethics education, there has been no prior exploration or illustration of process recording within this realm.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.081 | 0.135 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".