Autoethnography, African studies, and whiteness: problems of cultural appropriation
Bibliographic record
Abstract
Autoethnography can be a powerful analytic tool.It is a form of research that is like autobiography and ethnography rolled into one.It centres the author's experience as a source of data but does so in a way to speak analytically about sociological and cultural features of a community the author is part of.Drawing from one's personal experience can lend power and authority to the author's voice; it can drive a narrative forward; it can provide a sympathetic character for a reader to connect with.It can also centre the author in ways that reinforce their own power and privilege.By referencing one's own experience in a fieldsite, for example, one skirt issues of permission, consent, or local approval by highlighting the author's own subject location and personal experience.For close to 50 years, anthropology has been witness to a reflexive practice, one in which the author writes themselves into their work (Marcus and Fischer 1986).This is an approach that bears strong similarities with autoethnography.The difference for me, however, is that the autoethnographer draws from an authentic place of belonging, while the reflexive anthropologist, though clearly present, is a visitor seeking to build connections.Reed-Danahay argues "autoethnography's main contribution to understandings of human experience is that it troubles the persistent dichotomies of insider versus outsider, distance and familiarity, objective observer versus participant, and individual versus culture.Because it assumes that ideas of the self are culturally constructed and socially enacted, autoethnography has potential for fruitful explorations of relationships between ethnographic researchers and their interlocutors.Autoethnography addresses a desire to incorporate the subjectivity of the researcher as well as those who are the focus of research into ethnographic writing and other forms of reportage.It works against ideas that social research should reflect the viewpoint of an "objective" observer" (2019:2).At the same time, Reed-Danahay finds there is no explicit agreement or consensus on what autoethnography is as a method or a style, but rather a lose body of works self-identifying under a common label.
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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 | 0.117 | 0.118 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.025 | 0.076 |
| Scholarly communication | 0.016 | 0.031 |
| Open science | 0.004 | 0.021 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".