Bibliographic record
Abstract
Globalization has been accompanied by a decline in unionization; however, while globalization presents extremely serious challenges to unions, globalization does not necessarily result in weakened unions. It is important for unions to identify and utilize ways to increase and leverage union power that are responsive to the pressures of globalization. Companies frequently introduce training during restructuring efforts aimed at remaining competitive in a global environment. This paper describes a joint union-management training program which offers an example of a pro-active union approach to joint training initiatives. The training took place in early 2004 in a typical paper mill in central Wisconsin. While the training was designed and undertaken in response to various competitive pressures, the content of the training was primarily determined by employee focus groups. The training design is examined against criteria for successful union involvement in joint ventures. The paper argues that, while joint ventures typically address management's production concerns at the expense of labor, a pro-active union can work to ensure that benefits also accrue to the union. Recent literature on union power in a globalized economy suggests that this training model could be used in other industries to enhance union knowledge and power.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.023 | 0.002 |
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".