The Predictors of Unmet Demand for Unions in Non-Union Workplaces: Lessons from Australia
Bibliographic record
Abstract
In this study, we examine the predictors of unmet demand for unions in non-union workplaces, using theAustralian Worker Representation and Participation Survey(AWRPS). Unmet demand is defined here, as those employees in non-union workplaces who would be likely to join a union if one were available. We argue that this is the first study in Australia to examine the predictors of unmet demand in non-union workplaces, and, that this is an important line of inquiry given a rise in non-union workplaces and never members in Australia, alongside declining union density and membership numbers. Drawing on three strands of existing literature, namely the individual propensity to unionize, the rise and characteristics of non-union workplaces and alternative forms of representation, and, managerial responsiveness to employees and unions, we develop and test four hypotheses. Our results show, controlling for a range of personal, job and workplace characteristics, that there are two significant predictors of the willingness to join a union in non-union workplaces: perceived union instrumentality (Hypothesis 2) and perceived managerial responsiveness to employees (Hypothesis 4), whereby employees who perceive that managers lack responsiveness are more likely to want to join a union if one were available. These results show that unions must try to enhance their instrumentality in workplaces and could be more effective in recruiting if they targeted never members. The results also show that unions need to have some gauge (measure) of how responsive managers are to employees, and that they can leverage poor responsiveness of managers for membership gain and the extension of organizing. In the final analysis, an understanding of the predictors of unmet demand for unions in non-union workplaces has implications for Australian unions’ servicing and organizing strategies, and for their future growth prospects.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".