Bibliographic record
Abstract
In this paper I explore how members of a Sufi community in Egypt use dream‐stories as examples to evoke an otherwise invisible realm, and how I, in turn, use their stories ethnographically. My Sufi interlocutors use examples to invite others into the realm of the imagination, to draw listeners into the shaykh's spiritual aura, and to offer a model for emulation that sometimes triggers similar experiences in others. Their approach to examples poses a challenge to the logic of representation, in which a particular stands in for a larger whole. Instead it points to an evocative logic in which examples do not merely represent; they also do things. Whereas ethnographic examples tend to oscillate between representation and evocation, referential language ideologies largely obscure the example's evocative power. I suggest that my interlocutors’ use of, and approach to, examples can help us think about the example as evocative and performative, including the ways in which examples act upon and through anthropologists.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.049 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".