Unsettling the self: Autoethnography and related kin
Bibliographic record
Abstract
Abstract Autoethnography, intimate ethnography, and ethnographic memoir have become increasingly central modes of anthropological writing. Although this trend has historical precedents, as found in the work of Zora Neale Hurston, Ruth Behar, and others, this two‐part special section explores the directions this work is taking, the potential contributions of such writing, and how we might analyze this trend. What does the expansion of these anthropological subgenres tell us both about our times and anthropology? How does “unsettling the self” require rethinking not only boundaries between selves and others, but our roles as anthropologists and our discipline in order to produce writing that, as Behar suggests, “does not alienate ourselves from our ourselves?” How does “unsettling the self” also entail, as Anand Pandian observes, “unsettling the world” around us, including explorations of contemporary capitalism, settler colonialism, racial politics, or the agency of natural environments or nonhumans? What are the ethical questions and the limits engendered by such work, and what might such trends bode for anthropology's future? This special section integrates examples of these growing anthropological subgenres alongside efforts to theorize this mode of writing as we attempt to answer Alisse Waterston's provocation: What is such work potentially “good for?”
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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 | 0.016 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.018 | 0.024 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".