The Value of Autoethnography for Field Research in Transcultural Settings
Bibliographic record
Abstract
Mary Louise Pratt uses the term autoethnography to refer to those instances in which members of colonized groups strive to represent themselves to their colonizers in ways that engage with colonizers' terms while also remaining faithful to their own self-understandings. This paper extends Pratt's conceptualization of autoethnography and describes how it may be used to inform field research in transcultural settings in the formerly colonized world. Drawing from research in a village in northern Pakistan, we argue that approaching fieldwork with an “autoethnographic sensibility” can yield important epistemological, methodological, and political insights into our research practices. The paper concludes by suggesting that these insights extend beyond a postcolonial, or even cross-cultural, research context, to inform more general debates in human geography about how to achieve a critical and reflexive research practice.
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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 | 0.113 | 0.135 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.014 | 0.063 |
| Scholarly communication | 0.017 | 0.025 |
| Open science | 0.003 | 0.021 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 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".