Recuperating and (re)learning the language of <i>autogestión</i> in Argentina’s <i>empresas recuperadas</i> worker cooperatives
Bibliographic record
Abstract
This article homes in on the recuperative and learning dimensions of Argentina’s empresas recuperadas por sus trabajadores worker cooperatives (ERTs, worker-recuperated enterprises). Drawing on the author’s sociological, ethnographic, and political economic work with Argentina’s ERTs since 2005, the article theorizes autogestión – the collective self-management of associated labour – from its take up by ERT movement protagonists. The article explores and theorizes how ERT workers come to practice autogestión, what they actually take back in the process of occupying and controlling the formerly capitalist workplaces that had employed them, and how their projects of cooperative production are ensconced in a ‘language of autogestión’ – recuperating and (re)learning other economic notions and practices against and beyond capitalocentric discourses. Grounded in a class-struggle Marxist perspective, the paper ultimately proposes that ERT workers collectively recuperate three overarching dimensions of productive life from capital: (1) the self-valorization of living labour, (2) cooperation in the labour process, and (3) the socialization of surpluses and wealth. These recuperations of autogestión, the article concludes, (their ‘language of autogestión’) offer evocative suggestions for envisioning less-exploitive forms of work, more socially just and democratic workplaces, and for challenging and beginning to move beyond neoliberal logics.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.015 |
| Scholarly communication | 0.004 | 0.003 |
| 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".