Navigating the emotional challenges of ethnographic accounting research: notes from first-time ethnographers
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose This study aims to better understand the emotional challenges that inexperienced accounting researchers may face in conducting ethnographies. To do so, the authors use Arlie Russell Hochschild’s (1979, 1983) notions of “feeling rules” and “emotion work” to shed light on the possible nature and impact of these challenges, and how her ideas may also become fruitful for academic purposes. Design/methodology/approach The authors take a reflective approach in sharing the raw observation notes and research diaries as first-time ethnographers in the area of management accounting. The authors use these to analyze “unprocessed” experiences of emotional challenges from the fieldwork and how the authors learned to cope with them. Findings The authors illustrate how emotional challenges in conducting ethnographies can be rooted in a clash with prevalent feeling rules of certain study situations. The authors explore the conditions under which these clashes occur and how they may prompt researchers to respond through means of emotion work to (re-)stabilize those situations. Based on these insights, the authors also discuss how wider conventions of the accounting academy may contribute to emotional challenges as they stand in contrast to principles of ethnographic research. Originality/value There remains a tendency in the accounting domain to largely omit emotional challenges in the making of ethnographies, especially in writing up studies. In this paper, the authors are motivated to break this silence and openly embrace such challenges as an asset when the authors talk about the process of creating knowledge.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.034 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.013 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it