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Record W3184994334 · doi:10.1111/amet.13020

Anti‐colonial friendship

2021· article· en· W3184994334 on OpenAlexaff
Darren Byler

Bibliographic record

VenueAmerican Ethnologist · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicChina's Ethnic Minorities and Relations
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsFriendshipColonialismStorytellingGender studiesSociologyMasculinityNarrativeAnthropologyPolitical scienceLiteratureSocial scienceLawArt

Abstract

fetched live from OpenAlex

ABSTRACT In Northwest China, young Uyghur men foster friendships with one another as they flee colonial dispossession in their villages and migrate to the city. These friendships, which ultimately offer forms of protection in these migrants’ lives, are enacted through storytelling about colonial violence. Their storytelling is best understood as a processual staging of social life, one that holds in tension the violence of ethnoracialization and the palliative care of homosocial friendships. The stories of police brutality and job discrimination that these young men tell are an everyday enactment of the trauma staged in Uyghur‐language fiction about colonial alienation—a narrative form that both inspires Uyghurs to tell their stories and, in turn, is inspired by that experience. In a similar way, storytelling and anti‐colonial friendship can also pull ethnographers into relations of intersubjective obligation that shape their anthropological practice of listening and writing. In some contexts, then, anthropology itself can be regarded as the work of anti‐colonial friendship. [dispossession,anti‐colonial friendship,masculinity,storytelling,police,colonial violence,anthropology,Uyghur,China]

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.056
Threshold uncertainty score0.112

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0100.005
Scholarly communication0.0020.001
Open science0.0000.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.031
GPT teacher head0.356
Teacher spread0.325 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2021
Admission routes1
Has abstractyes

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