Introduction: Ethnography, Performance and Imagination
Bibliographic record
Abstract
This introduction to the thematic section entitled “Ethnography, Performance and Imagination” explores performance as “imaginative ethnography” (Elliott and Culhane 2017), a transdisciplinary, collaborative, embodied, critical and engaged research practice that draws from anthropology and the creative arts. In particular, it focuses on the performativity of performance (an event intentionally staged for an audience) employed as both an ethnographic process (fieldwork) and a mode of ethnographic representation. It asks: can performance help us research and better understand imaginative lifeworlds as they unfold in the present moment? Can performance potentially assist us in re-envisioning what an anthropology of imagination might look like? It also inquires whether working at the intersections of anthropology, ethnography, performance and imagination could transform how we attend to ethnographic processes and products, questions of reflexivity and representation, ethnographer-participant relations and ethnographic audiences. It considers how performance employed as ethnography might help us reconceptualise public engagement and ethnographic activism, collaborative/participatory ethnography and interdisciplinary research within and beyond the academy. Finally, this introduction provides a brief overview of the contributions to this thematic section, which address these questions from a variety of theoretical, methodological and topical standpoints.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.028 | 0.005 |
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".