Bibliographic record
Abstract
This article examines the potential of a transdisciplinary ethnographic approach that bridges ethnography, performance, storytelling, and imagination to contribute to an activist research practice within anthropology and other disciplines. It focuses on my current research project that studies, by means of dramatic storytelling, the impact of migration on Polish Romani women’s experiences of aging. In the dramatic storytelling sessions, the ethnographer and the interlocutor stepped into character and co-performed fictional stories loosely based on their own lives. Situating the project within the context of an “imaginative ethnography” that is concerned with people’s imaginative lifeworlds, and methodological experimentations at the ground level of fieldwork, this article discusses the ways the project challenged traditional conceptions of engagement and advocacy. It considers the silence—“quiet theatre”—that engulfed the interlocutor–ethnographer interactions in the storytelling sessions as a form of radical empathic politics that works through affect, projective approximation, and empathy. In doing so, the article proposes a conceptualization of interventionist research practice as a contextually specific particularity that takes to task the meanings of politics in academic activism.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.070 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.005 |
| 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".