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Record W4410199887 · doi:10.1111/aman.28080

Escribir en comunidad: Construcción de relaciones y responsabilidad en la producción de conocimiento

2025· article· en· W4410199887 on OpenAlexaff
Jordi A. Rivera Prince, Emily M. Blackwood, Madeleine Landrum, Emily B. P. Milton, Elizabeth Leclerc, 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
FieldSocial Sciences
TopicHigher Education and Sustainability
Canadian institutionsWestern University
Fundersnot available
KeywordsHumanitiesGeographyPolitical scienceArt

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)0.003
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.580
Threshold uncertainty score0.958

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.044
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.006
GPT teacher head0.406
Teacher spread0.399 · 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 designTheoretical or conceptual
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

Citations0
Published2025
Admission routes1
Has abstractyes

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