The Future of Private Sector Unionism in the United States
Bibliographic record
Abstract
Preface 1. Introduction 2. The Future of Private Sector Unions in the U.S., Seymour Martin Lipset and Ivan Katchanovski 3. Accounting for the Decline of Unions in the Private Sector, 1973-1988, Henry Farber and Bruce Western 4. The Twilight for Organized Labor, Leo Troy 5. Information Technology, Unions, and the New Organization: Challenges and Opportunities for Union Survival, Anthony Townsend, Samuel DeMarie, and Anthony Hendrickson 6. Private Sector Union Density and the Wage Premium: Past, Present, and Future, Barry Hirsch and Edward Schumacher 7. Unionism as Value-Adding Networks: Possibilities for the Future of U.S. Unionism, Saul Rubinstein 8. The Fall and Future of Unionism in Construction, A.J. Thieblot 9. Learning from Each Other: A European Perspective on American Labor, Edmund Heery 10. Labor's Love Lost? Changes in the U.S. Environment and Declining Private Sector Unionism, Edward Potter 11. Human Resource Management Practices and Worker Desires for Union Representation, Jack Fiorito 12. New Strategies for Union Survival and Revival, Nancy Mills 13. Labor Unions: Victims of Their Own Political Success? James Bennett and Jason Taylor 14. Mandatory Agency Shop Laws as an Explanation of Canada-U.S. Union Density Divergence, Daphne Taras and Allen Ponak 15. Intensity of Management Resistance: Understanding the Decline of Unionization in the Private Sector, Morris Kleiner 16. Strategy for Labor, Samuel Estreicher 17. The Future of Private Sector U.S. Unionism: Did George Barnett Get It Right After All? Bruce Kaufman 18. Conclusion
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.024 | 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".