Hero or Villain? A Cohort and Generational Analysis of How Youth Attitudes Towards Unions Have Changed over Time
Bibliographic record
Abstract
Abstract Our study examines youth attitudes towards unions over a 40‐year period to try and understand whether today's young workers might be the ‘hero’ or the ‘villain’ in the tale of declining union membership rates in the United States. Using nationally representative time‐lag data from high‐school seniors ( N = 104,742) spanning 1976–2015, we conducted time trend, birth cohort and generational analyses to provide an ‘apples to apples’ comparison of how youth have felt about unions at different points in time. We found that contemporary youth (or Millennials) hold similar union attitudes to those who came before them, though what predicts those attitudes has changed over time. Strikingly, we also found that the proportion of young people who hold no opinion about unions has more than doubled over the period under study, steadily rising from 14 per cent in 1976 to 33 per cent in 2015. This sizeable proportion of ‘agnostic’ youth should be alarming to unions, yet it also provides them with opportunities to shape youth attitudes through targeted outreach efforts.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".