Ancestral transplantation as a matter of gender: Narrating us, Wigudun
Bibliographic record
Abstract
In this short essay, we want to revive some reflections reported to us by Yineth Muñoz, a person of Guna origin, an Indigenous people from Panama, to think with her what gives form to gender. We intend to critically imagine (Hartman, 2019) what happens to this concept – a technology, a somato-political fiction, and a material cutout – once the elaborations, concerns, and agency recalled and presented by Yineth's narratives traverse it. In this sense, the effort is not to explain Guna's gender, meaning to understand it as an object of ethnological elaboration. We would like to consider what comprises gender when it comes to metaphorizing – as an equivocal concept – the problems and reflections posed by Yineth. We will begin by going back to Yineth's considerations, which implicate gender in a series of other procedures and recursiveness that, we think, assist in raising questions to complicate some of the metaphors underlying mainstream descriptions concerned with matters of gender. Therefore, ours is an exercise concerned with a transfeminist engagement with the problem: to implicate oneself in it, not to explain it.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.015 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".