Bibliographic record
Abstract
successful campaign to organize printing industry workers in the U.S. used innovative strategies that may be useful and instructive for Canadian unions. The campaign was initiated in 2002 by the Graphic Communications International Union (GCIU)1 at Quebecor World, a Canadian company, and one of the world’s largest commercial printing companies. Quebecor, which began as a small Quebec-based newspaper publisher, operates 150 commercial printing facilities in 16 counties and has revenues of over $6 billion. The Graphic Communications International Union has since merged with the International Brotherhood of Teamsters (IBT) in the United States and the Communications Energy and Paperworkers Union of Canada (CEP) in Canada. At the time of the campaign, GCIU had approximately 80,000 members in the US and Canada, almost all of them in the printing, publishing or packaging industries. GCIU used a comprehensive campaign strategy of rank and file organizing, membership mobilization, corporate leverage and global solidarity to win a neutrality and fair election process agreement with Quebecor World and brought union representation to hundreds of printing industry workers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.033 | 0.006 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.031 | 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".