Introducing Workers’ Embedded Agency: Insights from the Brazilian Subsidiaries of a Multinational Corporation
Bibliographic record
Abstract
The relevance of subsidiary embeddedness in a macro-institutional environment can in no way overshadow the importance of the micro-political agency of social actors. While some researchers focus on local management’s “embedded agency,” we focus on a less-developed aspect: workers’ “embedded agency.” In order to do so, we propose an analytical model that is based on the Varieties of Capitalism model and its subsequent developments, but that also includes the workers as an active agent. This model allows us to observe the institutional resources that workers can actively mobilize. We specifically focus on the characteristics of industrial relations and education institutional sub-systems. We apply the developed analytical model to the case of the Brazilian subsidiaries of a highly global multinational corporation (MNC). Brazil represents a context where institutional constraints (i.e. corporatist industrial relations and a dualist education system) make workers’ actions the least favorable. Moreover, the highly integrated organizational environment of the MNC further reinforces this aspect. In turn, this makes it more compelling to discover how workers can nevertheless strategically activate some resources to improve their conditions. We conduct a case study and collect empirical data through semi-structured interviews and documentary analysis. More specifically, we discuss three examples of workers’ “embedded agency” (i.e. election of a bilateral committee for the prevention of accidents; plant closure; and internship and training). These bring analytical attention to workers’ collective and individual actions as well as intra- and extra-subsidiary mobilization of institutional resources.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".