“They don´t represent us!” Opportunities for a Social Movement Unionism Strategy in Spain
Bibliographic record
Abstract
Our goal is to analyze strategies of union revitalization that have been successful elsewhere and have the potential to become so in Spain. Within these practices, Social Movement Unionism focuses on alliances with other groups to improve unions’ social efficiency. In this article, we address the applicability of the principles of Social Movement Unionism in the specific case of Spain. Given the transformations in the Spanish economy and labour laws tending towards further deregulation, Spanish unions have had to react. The emergence of new social movements such as theIndignadosorMareas Ciudadanas(civic tides) and the declining confidence in unions among the Spanish population, make this approach timely and appropriate. For this article, we will take certain aspects from the trade union revitalization debate and combine them with the main theories on New Social Movements. We will apply these approaches to a specific case study: The viability of cooperation between the largest Spanish trade unions and the recent social movements arising from theIndignadosmovement. For this purpose, we will primarily use data from secondary sources and transcripts of interviews conducted with unionists and social movement activists. With all these elements taken into consideration, we will conclude by showing the inhibiting and facilitating conditions for the development of a Social Movement Unionism strategy for the referred actors.
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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 | 0.007 | 0.003 |
| 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 | 0.006 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".