Balancing Emic-Etic Tensions in the Field-, Head-, and Text-Work of Ethnographic Management Accounting Research
Bibliographic record
Abstract
ABSTRACT Ethnographers must balance the tensions between the emic and etic dimensions of research. For example, they must simultaneously become an emic insider of the group studied, while at the same time retain their analytical distance to remain an etic outsider. This article discusses how these tensions manifest in head-, field-, and text-work by reviewing 52 self-declared management accounting ethnographies published between 1997 and 2017. The review shows that there is an (over-)emphasis on a realist tale-telling approach, in which the author’s voice is almost always effaced as tale-tellers detach themselves from the tales being told. As alternatives, we highlight confessional and impressionist tale-telling approaches. Although all three approaches offer advantages for addressing the emic-etic balance, they also all involve sacrifices. Thus, we urge researchers to give deeper consideration to text-work choices in management accounting ethnographies.
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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 | 0.061 | 0.095 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.008 | 0.030 |
| Scholarly communication | 0.016 | 0.015 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".