Bibliographic record
Abstract
I first met Sneja Gunew at a Women’s Studies conference in Wollongong in 1981. Together with Louise Adler, she gave a presentation, ‘Method and Madness in Female Writing’, which made a challenging intervention into this gathering. Intervention was her preferred practice of intellectual responsibility and challenge her characteristic style. I joined the staff at Deakin in 1985-1986, and during that time Sneja and I became firm friends, and her work influenced mine considerably. As well as teaching literary studies at Deakin we both contributed to the interdisciplinary course, ‘Women and Social Change’, which led to Sneja’s editing the companion volumes Feminist Knowledge as Critique and Construct and A Reader in Feminist Knowledge. As well as her influential work on multiculturalism and her interventions into the rapidly developing world of feminist studies, Sneja took on the male establishment, mainly through a series of contributions to debates around the publication of Ken Ruthven’s Feminist Literary Studies (1984) and subsequently on ‘the new humanities’. On the brink of her departure for Canada in 1993 Sneja’s essay, ‘Feminism and Difference,’ again challenged any feminist work that claimed to speak for all women, including those minoritised in terms of race or class. Whether the issue was women’s liberation or multiculturalism, creative practice or cultural policy, Sneja always emphasised difference and diversity, and made it her business to deconstruct any assumptions of uniformity, or of a neutral speaking position.
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.017 | 0.033 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".