Solidarity in action: building worker-owned intersectional platforms in Latin America
Bibliographic record
Abstract
Abstract The project Worker-Owned Intersectional Platforms (WOIP) (Grohmann et al., 2025) brought together six organizations from Brazil and Argentina in the tech and delivery sectors experimenting with worker-led and intersectional approaches to digital technologies. In this interview, Rafael Grohmann, principal investigator of the project, spoke with Cecilia Munoz Cancela from FACTTIC/Código Libre, Joaquim Renato Alves from Señoritas Courier, Maraiza Adami Pereira from MariaLab and Laura Arcuri, from FACTTIC/Animus organizations about what the WOIP project allowed them to imagine, what their collectives are already building, the challenges they face, how to construct coalitions without erasing differences, and what message they want to send to academics and universities. They are demonstrating embodying solidarity in action, which comes from Latin America, from the intersectional working class, with all its complexity.
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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 | 0.011 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.022 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.002 | 0.030 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".