Union Participation Through a Generational Cohort Lens: Improving Participation Across Cohorts
Bibliographic record
Abstract
This thesis consists of three mixed-method (interview, survey) research papers that share a common goal -to increase our understanding of the extent to which generational cohort impacts union participation and more broadly, renewal.Paper one sets the context for the thesis, exploring differences in attitudes towards and perceptions of unions between Baby Boomers (born 1946-1964), Generation X (1965-1979), and Millennials (1980-2000).Overall, we found that Boomers differ significantly from Gen Xers and Millennials, while the latter two groups were more alike than different.Specifically, Boomers' attitudes and perceptions towards/of unions were typically more positive than those of Gen Xers and Millennials.Further, Boomers appeared to connect to unions on both ideological and instrumental levels, while Gen Xers and Millennials appeared to connect mostly based on the instrumental gain unions can provide their members.Paper two seeks to learn more about how union members of different cohorts conceptualize different levels of participation activity (ie: Active, Passive, Inactive), comparing these emic conceptualizations to the etic understanding of active and passive participation found in the union literature.While we found a need for more nuance in the area, we generally found that union members' emic conceptualization of active and passive participation overlap with the etic descriptions of these concepts held by researchers within the union literature -those who the literature would say participate more actively described their own participation as active, while those who participate more passively tended to describe their activity as such.Paper three compares the state of current union communication practices with our participants' preferences, identifying any gaps, while also exploring what each cohort believes the union is doing well and where it needs to improve with respect to informing members about Preface At the time of this dissertation's submission, paper one had been accepted for publication in The Journal of Social Psychology (2019), paper two had been accepted for publication in Industrial Relations Journal, and paper three received a revise and resubmit from the British Journal of Industrial Relations.Chris Smith is the first author and Dr. Linda Duxbury (thesis supervisor) the second author of all three papers.
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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 | 0.043 | 0.068 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".