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Record W1941073851 · doi:10.1111/1467-9655.12170

How to do things with examples: Sufis, dreams, and anthropology

2015· article· en· W1941073851 on OpenAlex
Amira Mittermaier

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of the Royal Anthropological Institute · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicIslamic Studies and History
Canadian institutionsUniversity of Toronto
FundersSocial Science Research Council
KeywordsRealmEvocationPerformative utteranceAestheticsRepresentation (politics)SociologyDreamNarrativeEpistemologyPower (physics)IdeologyHistoryPhilosophyLinguisticsAnthropologyPsychologyPolitics

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.691
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.006
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.055
GPT teacher head0.310
Teacher spread0.255 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it