Bibliographic record
Abstract
This collection of essays arose from a conference held to mark the silver anniversary of the Australian Sex Discrimination Act (1984). The collection has two aims: first; to honour the contributions of both the spirited individuals who valiantly fought for the enactment of the legislation against the odds, and those who championed the new law once it was passed; secondly, to present a stock-take of the Act within the changed socio-political environment of the 21st century. The contributors present clear-eyed appraisals of the legislation, in addition to considering new forms of legal regulation, such as Equality Act, and the significance of a Human Rights Act. The introduction of a proactive model, which would impose positive duties on organisations, is explored as an alternative to the existing individual complaint-based model of legislation. The contributors also pay attention to the international human rights framework, particularly the Convention on the Elimination of all Forms of Discrimination against Women and the UN Declaration on the Rights of Indigenous People. The essays are illuminated by recourse to a rich vein of historical and contemporary literature. Regard is also paid to the comparative experience of other jurisdictions, particularly the UK and Canada
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.010 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".