Whose Fault Is It? Asking the Right Questions When Trying to Address Discrimination
Bibliographic record
Abstract
The Australian Government’s announcement that it intends to ‘consolidate’ federal anti-discrimination laws has prompted debate about how these laws could be reformed rather than merely reformatted. In this article we compare Australian anti-discrimination laws with equivalent laws in the United Kingdom and Canada to illuminate the conception of discrimination that underpins each law and to prompt further debate about the appropriateness of the Australian regulatory model. If equality is accepted as a social good that benefits all members of a society, regulation that requires responsibility for addressing inequality to be shared is justified. In nations comparable to Australia, such as the UK and Canada, this has been accepted and built into the design of equality laws. The regulatory trend discernible in these jurisdictions is clearly a move away from an individual fault-based model of discrimination regulation like Australia’s which targets only discrimination that can be traced to a wrong-doer. The move is toward a regulatory model that castes a wider net requiring duty-holders not merely to refrain from wrong-doing but also to make at least reasonable efforts to eradicate discrimination and promote equality. In the United Kingdom this is illustrated by the introduction of positive equality duties to supplement the traditional anti-discrimination laws. Alternatively, Canada’s complaint-based system of anti-discrimination laws imposes a limited ‘positive’ obligation on duty holders to provide reasonable accommodation to members of all protected groups. In contrast, Australia’s anti-discrimination laws have not significantly developed since their inception, leaving Australia with ineffective laws and lagging behind international consensus on human rights and equality. To avoid achieving nothing more than ‘consolidation’ of narrow, inadequate, fault-based laws, we need to ask better questions. Addressing inequality is not just about fault.
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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 | 0.032 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.020 | 0.044 |
| Scholarly communication | 0.015 | 0.028 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.020 | 0.035 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".