Pedagogies of awareness and subterfuge: The interpretive labor of domestic worker organizing in Buenos Aires
Bibliographic record
Abstract
Abstract In 2013, Argentina passed Law 26844, transforming domestic workers' status from “servants,” with almost nonexistent labor rights to “workers,” with rights virtually equal to all other workers under the law. Since then, domestic workers' legal equality has stood against the intersectional inequalities they still endure vis‐à‐vis their employers and that challenge advancement to their labor rights. Based on ethnographic research conducted in Buenos Aires between 2016 and 2018 with domestic workers' rights advocates, this article investigates how they contend with these challenges. It examines the interpretive labor they engage in as they encourage workers to undertake situated strategies to advance their labor rights. These strategies consist of increasing workers' knowledge of their rights while, simultaneously, encouraging them to execute dissembling practices to claim and access them. These dissembling practices allow workers to perform the appropriate behaviors according to intersecting master categories of social differentiation in a context of systemic intersectional inequality. Concurrently, they enable workers to avoid direct confrontation with their employers, thus helping them to enforce their rights without jeopardizing their means of subsistence. Through these pedagogies of awareness and subterfuge, advocates create a space in which the law can be effected, promoting workers' agency without compromising their livelihoods.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".