How much would US union membership increase under a policy of non‐exclusive representation?
Bibliographic record
Abstract
Purpose In light of the low‐union density and a huge representation gap in the US representation system. The purpose of this paper is to examine the effectiveness of the system under majority rule and to provide some empirical evidence on how much union membership would increase in the USA if a policy of non‐exclusive representation, as adopted in New Zealand, are to be implemented. Design/methodology/approach The sample for the study consists of 227 New Zealand organizations, employing over 180,000 workers. Logistic regression is used for the analysis with the dichotomous dependent variable indicating whether there is majority union support. Findings If the USA allowed and supported minority unionism, union membership could increase by 30 percent or more. Workers in smaller, private‐sector organizations outside healthcare, education, and manufacturing are most disadvantaged by the majority‐rule system. Practical implications Given that many workers' needs for representation have not been addressed by the current US majority rule system, consideration of minority representation to enhance representation effectiveness and understanding its implications are of critical importance, especially for a democratic society. Originality/value The paper offers empirical data on the implications of a change of the US representation system and proposes three options for incorporating minority representation.
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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 | 0.011 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".