Balancing Breadth and Depth in Qualitative Research: Conceptualizing performativity through multi-sited ethnography
Bibliographic record
Abstract
While performativity, as a theoretical concept, has gained much purchase in the field of management and organization studies (MOS), there remains a dearth of empirical work operationalizing the idea. In this article, we argue that empirical studies on performativity in organizational settings have been scarce mainly due to two methodological challenges: (1) the problem of breadth, and (2) the problem of depth. In terms of breadth, to study how a cultural/symbolic construction becomes performative, researchers need to follow threads of meanings and practices that stretch in time and space beyond the scope of what most methodologies afford. In terms of depth, studying performativity requires an in-depth, contextualized understanding of how people live in and through the symbolic world, which few methodologies possess the analytical resources to unravel. We offer multi-sited ethnography as a promising methodological approach that has the potential to concomitantly overcome both challenges. Through a vignette of a study that one of the authors conducted in the Occupied Palestinian Territories, we illustrate how multi-sited ethnography enables empirical research on performativity in MOS.
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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 | 0.114 | 0.095 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.007 | 0.039 |
| Scholarly communication | 0.009 | 0.015 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".