Building Union Power Across Borders: The Transnational Partnership Initiative of IG Metall and the UAW
Bibliographic record
Abstract
This is a case study of how the transnational cooperation between two unions – IG Metall in Germany and the United Automobile Workers (UAW) in the United States – was put on a new trajectory. It is a template for the challenges unions face in adapting their nationally oriented self-interest toward building transnational solidarity and being able to leverage global corporate power in defence of workers’ interests across borders. Using the power resources approach, it highlights the unions’ transnational strategy built on mobilising associational and institutional resources. Understanding their make-up and utilisation became crucial in the process as limits to institutional power without involvement and mobilisation on the ground became evident. The case study focuses on the initiation and preparation phase of a more comprehensive organisational cooperation, culminating in a formal agreement to establish a Transnational Partnership Initiative (TPI) in 2015. While no organising gains were made in this phase – indeed, only losses – it was crucial for building trust and mutual understanding, as well as for actively promoting a broadly based anchoring of the TPI in terms of policy in both unions. The case study’s conclusions are generally positive on this count; yet they are preliminary as the overall project is a work-in-progress and its basis of support beyond the two unions (societal power) is still untested.
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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 | 0.004 | 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 | 0.013 | 0.010 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".