Bibliographic record
Abstract
Abstract The labour movement and the human rights movement have long converged, with labour unions having become a significant actor in Australian human rights scrutiny processes and able to influence legislation concerning marginalized populations. In this paper, we explore the influence of Australian labour unions on human rights in relation to two population groups—people who use drugs and LGBTIQA+ people. This is based on a detailed examination of labour unions’ submissions to the development and review of human rights charters in three Australian jurisdictions—the Australian Capital Territory, Victoria, and Queensland—and legislative scrutiny committees in these jurisdictions, with attention to how labour unions adopt human rights analyses and arguments in their submissions. Our analysis has found that there are some areas in which labour unions are strong advocates for advancement of human rights—namely, workers’ rights and women’s rights—and some areas in which labour unions are critical of human rights advancements—namely, criminals’ rights and the right to health. Furthermore, some labour unions have tensions with human rights generally. Often the interest of labour unions in managing public behaviour that impacts workers may be in tension with human rights concerns, and the dominance of labour unions in legislative scrutiny processes can raise issues for human rights-compatible law reform for people who use drugs and LGBTIQA+ people. This paper explores these tensions and charts future directions for research on labour unions and human rights.
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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 | 0.014 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.018 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".