How True is What Everyone Knows? Board Avoidance, First Contract and the Organizing Versus Servicing Model
Bibliographic record
Abstract
Three key ideas about how unions should best respond to declining union membership have become gospel in the United States labor community. First, unions should avoid the National Labor Relations Board (NLRB), because it is hostile to worker rights. Second, the biggest problem facing unions is the failure to win elections and then to secure first contracts. Third, the traditional servicing model of union representation is bad, and the organizing model is good. Numbers two and three mean that unions should move as many resources as possible into organizing and, implicitly, away from servicing current members.We examine these contentions in the context of data derived from a study of NLRB decisions over a fifteen-year period from 1980-1994. Our data show that rather than being hostile to worker rights, unions have fared far better before the Board than has been suggested. Furthermore, we found that the current focus on organizing, if it means removing support for collective bargaining in established relationships, is a dangerous policy and may, in the end, undercut organizing success.
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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 | 0.012 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.033 |
| Scholarly communication | 0.011 | 0.022 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".