Scholarly Book Publishing in Australia: The Impact of the Last Decade
Bibliographic record
Abstract
This article lends perspective on developments and trends in Australian scholarly book publishing from the late 1980s to early 2002. Australia, with a population of 19 million, has current economic growth rates of 3.7 per cent with forecasts well ahead of other Western industrialised economies. The economy (GDP) grew by 46.2 per cent between 1990 and the end of 2001, while the population increase was 13.6 per cent. Australia is the twelfth-largest book market in the world, and the third-largest English-language market, but it is still less than 5 per cent of the market for international scholarly books. According to an international survey 1 , Australia spent on books of all categories 69 per cent per capita of what US citizens spent, but this still represented only 5 per cent of the US dollar total, and 26 per cent of the UK's expenditure.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.014 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 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".