Multi-event ethnography: doing research in pluralistic settings
Bibliographic record
Abstract
Purpose – The purpose of this paper is to overcome the challenges of doing research in pluralistic settings by performing multi-event ethnographies. The proposal redirects the efforts of longitudinal data collection toward field-configuring events (FCEs), wherein multiple organizations with divergent perspectives over an issue are strategizing in concentrated efforts, at the same time and space. The authors apply traditional ethnographic tools in this understudied setting. On the one hand, these tools allow for a thick description that results in in-depth accounts of actors within FCEs. On the other hand, they provide flexibility because they can be used in complementary ways. Design/methodology/approach – The authors propose the use of three interconnected ethnographic tools in multiple events: shadowing, practitioner's diary and researcher's reflexive journal. Findings – The illustration of an ongoing research project showed how the approach helped the researchers to follow a practitioner in multiple discursive spaces but also to see how the practitioner, even with a different status in a later FCE, transported a deviant practice that denounces his persistent disadvantaged position in the field. The approach delineated here allowed the researchers to open a new window for the appreciation of the activities of marginal actors fighting against hegemonic discourses. Research limitations/implications – The application of the shadowing technique might be challenging. Attention might also be paid to the implications of previous FCEs to current dynamics. Practical implications – The tools developed in this approach have a large potential to have practical implications, as the practitioner accounts of the phenomenon in question are at the center of the data collection and analysis. Originality/value – The proposal contributes to the literature on organizational ethnography by drawing attention to the importance of tracking multiple events, not only different sites, to unveil organizational practices in pluralistic settings as events progress over time.
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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 | 0.057 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.010 | 0.022 |
| Scholarly communication | 0.011 | 0.015 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".