Bibliographic record
Abstract
In this article, I address the editors’ (Sidnell and Lambek) arguments about the immanence of the ethical, in contrast to different questions concerning ‘ordinary ethics’ recently raised in anthropology. I will do so by asking what an ‘ordinary realism’ might entail for anthropology, and the way it could possibly help us to appreciate the difficulties inherent in our work of approaching the issues at stake in the reality we face, whether during fieldwork or back home. I will proceed and develop this questioning with the help of some contemporary philosophers who have sustained and enriched the debates on ethics in anthropology. I will illustrate the difficulty of ordinary realism for anthropology with an example taken from a particular moment during my field in Zanzibar where dreams and witchcraft entered the scene.
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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 | 0.030 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.083 |
| Scholarly communication | 0.009 | 0.017 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.003 | 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".