Glitter and Graffiti: Labour, Expertise and the Feminist Remaking of Mexican National Heritage
Bibliographic record
Abstract
Abstract In 2019, thousands of women took to the streets in Mexico City to protest gender-based violence. The demonstrations were characterised by the defacement of iconic monuments, which was widely condemned. But the protests also ignited widespread political mobilisation, including by a group of women restorers who, despite being designated to clean the monuments, refused to perform their work and publicly defended the protesters. By withholding their labour and their ostensible duty to the state and to the nation, the restorers’ actions helped to transform narratives around feminism, protest and the meaning of national heritage. Based on a case study of this previously depoliticised group of art restorers who went on to become one of the most important faces of Mexico's feminist movement, this article argues that political mobilisation can be rooted in and directly linked to people's labour and professional expertise.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".