Escribir en comunidad: Construcción de relaciones y responsabilidad en la producción de conocimiento
Bibliographic record
Abstract
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.
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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 | 0.023 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.022 |
| Scholarly communication | 0.015 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".