How do trade unions manage themselves? A study of Australian unions’ administrative practices
Bibliographic record
Abstract
Dedication We gratefully acknowledge the invaluable contributions to the research that we discuss here by our dear friend and colleague the late Dr Sandra Cockfield; we miss her greatly. We dedicate this article to her. For a tribute to her see www.monash.edu/vale/home/articles/vale-dr-sandra-cockfield . The article discusses issues rarely addressed in research on Australian unions: the internal management policies and practices of unions, including human resource management, budgeting and strategy formulation. Management matters because it creates processes and systems that focus activity on whatever objectives a union or other organisation wishes to achieve. Our main research question is ‘how do Australian unions manage their employees, budgets, and strategies?’ Our study builds on earlier studies of US, UK and Canadian unions by adapting a survey instrument used in these countries. The Australian Council of Trade Unions (ACTU) asked national and branch unions to complete our online surveys. Of the unions surveyed, a majority of respondents use systematic human resource management policies and practices. They have also adopted strategic planning and budgeting practices. Echoing international findings, Australian unions have increasingly professionalised their administration. These findings are important since they have implications for how Australian unions deal with the challenges they face, including their revitalisation efforts and their responses to changing regulatory contexts.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".