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Record W4409758185 · doi:10.1111/aman.28070

Writing in community: Relationship building and accountability in knowledge production

2025· article· en· W4409758185 on OpenAlexafffund
Jordi A. Rivera Prince, Emily M. Blackwood, Madeleine Landrum, Emily B. P. Milton, Elizabeth L. Rodgers, Mónica Barnes, Elizabeth Chin, Christa Craven, Kristina Douglass, María José Figuerero Torres, María A. Gutiérrez, Sarah Herr, Lisa Hodgetts, Kirk A. Maasch, Kylie Quave, Danilyn Rutherford, Daniel H. Sandweiss

Bibliographic record

VenueAmerican Anthropologist · 2025
Typearticle
Languageen
FieldArts and Humanities
TopicDiscourse Analysis in Language Studies
Canadian institutionsWestern University
FundersSecretaría de Ciencia y Técnica, Universidad de Buenos AiresSocial Sciences and Humanities Research Council of CanadaUniversidad de Buenos AiresNational Science Foundation
KeywordsAccountabilityKnowledge productionProduction (economics)SociologyPolitical scienceKnowledge managementLawComputer scienceEconomics

Abstract

fetched live from OpenAlex

Abstract As anthropology reckons with its past, present, and future, anthropologists increasingly seek to challenge inequities within the discipline and academia more broadly. Anthropology, regardless of subdiscipline, is a social endeavor. Yet research often remains an isolating (though not necessarily solitary) process, even within research teams and in coauthorship contexts. Here, we focus on peer‐reviewed publication as the principal manifestation of knowledge production and propose a method for challenging division, hierarchy, power differentials, and adherence to tradition: writing in community . Writing in community is a collaborative form of writing that centers care, abundance, joy, and personal satisfaction over the individuality currently rewarded by the academy. This process engenders consensus, circumvents normative hierarchical research and writing, and promotes relationship building. Here, we experiment by inviting reviewers and editors into our community to collectively contribute to the writing process and reflect on that experience together. Ultimately, we challenge norms for scholarship, (co)authorship, and ways of knowing to offer a more equitable praxis of knowledge production. We propose that writing in community can help anthropologists enact values of multivocality and research transparency.

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 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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.867
Threshold uncertainty score0.983

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 studies0.0010.020
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 head0.048
GPT teacher head0.385
Teacher spread0.337 · 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 teacher head, not a consensus.

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
Published2025
Admission routes2
Has abstractyes

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