Documenting Traces of Latin American Feminist Movements: Digital Counterarchives as Sites of Memory and Resistance
Bibliographic record
Abstract
ABSTRACT Introduction Traces of protests from Latin American feminist movements (such as signs, feminist graffiti, etc.) tend to disappear quickly, both because these protest expressions are ephemeral and because institutions erase these traces before they can be documented and preserved. This erasure is a policy decision because it removes women's demands from the public space. Methods In this paper, we explain two approaches for documenting traces of Latin American feminist movements of the 2020s to prevent this erasure and address their ephemerality: one through a local digital counterarchive for a mid‐sized Mexican city (titled Huellas Incómodas) and another through a regional web counterarchive (titled Feminist Activisms in Latin America). Results Counterarchives are needed because they are sources to record, highlight, and legitimize women's knowledge and demands and because memory institutions lack policies on how to document and preserve feminist protests. Building digital counterarchives can resist this erasure of women's demands and unrest by documenting and preserving these protest traces, which we call Huellas Incómodas (uncomfortable footprints) because they expose the inaction of institutions to address gender‐based violence. Conclusion By constructing digital counterarchives, the erasure of women's demands and unrest can be resisted through the documentation and preservation of these protest traces. Termed Huellas Incómodas to underscore the inaction of institutions in addressing gender‐based violence, these counterarchives play a vital role in amplifying women's voices and demanding change.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".