Bibliographic record
Abstract
Purpose – The purpose of this paper is to explore ethical issues associated with using the shadowing method. Design/methodology/approach – Ethical issues that arose during a 12-week shadowing study that examined the work activities and practices of Canadian healthcare CEOs are discussed. Findings – Dividing the ethics process into two phases – those addressed by ethics committees (procedural ethics) and those that revealed themselves in the field (ethics in practice) – issues and relating to sampling, informed consent, researcher roles, objectivity, participant discomforts, the impact of research on participants, confidentiality, and anonymity are investigated. This paper illustrates that while useful, procedural ethics committees are unable to establish ethical practice in and of themselves. In response, it suggests that the concept of reflexivity be applied to ethics to help researchers consider the implications of using the shadowing method, and develop a contingency for possible challenges, before they enter the field. Practical implications – This paper provides researchers considering using the shadowing method with critical insights into some of the ethical issues associated with the method. A number of questions are posed and a number of suggestions offered as to how ethical practice can be attained in the field. Given practice-based similarities between shadowing and other qualitative methodologies such as participant observation and ethnography, many of the lessons derived from this case study are also pertinent to researchers using other techniques to examine organizational and management phenomenon. Originality/value – Building on the formal and critical discussion about the shadowing method ignited by McDonald (2005), this paper identifies and discusses ethical issues associated with the shadowing method that have not been examined in either ethics or research methods literature.
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 | 0.324 | 0.355 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.021 | 0.089 |
| Scholarly communication | 0.018 | 0.018 |
| Open science | 0.005 | 0.017 |
| Research integrity | 0.009 | 0.012 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".