Building ‘Critical Performativity Engines’ for deprived communities: The construction of popular cooperative incubators in Brazil
Bibliographic record
Abstract
Although worker cooperatives offer an organizational model that critical management scholars could adopt to demonstrate the utility of their normative ideals, little is known about how academia can contribute to the creation of worker cooperatives. Building on the concept of performativity and the case of the Technological Incubators for Popular Cooperatives in Brazil, we provide an account of constructing incubators for worker cooperatives across multiple universities. Our study uncovers the challenges that scholars face in performing the model of worker cooperatives by cognitively embedding actors within both economic and cooperative principles through teaching. Our results clarify the role of feedback loops, knowledge circulation, and the building of ‘chains of translation’ in the concrete manufacturing of worker cooperatives, and we show how universities can help develop a multilevel, flexible, and complex support network that enhances the performativity of the worker cooperative model. We advance the concept of a ‘critical performativity engine’ to describe the process whereby the first method for incubating cooperatives was developed and then translated across settings.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.015 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.001 | 0.001 |
| 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".