Researching Literacy and Numeracy Costs and Benefits: What is possible
Bibliographic record
Abstract
Assessing the social and economic benefits of investing in adult literacy and numeracy and the costs of poor adult literacy and numeracy, is largely uncharted territory in Australia. Some interest was evident in the late 1980s leading up to International Literacy Year, 1990 (for example, Miltenyi 1989, Singh 1989, Hartley 1989); however, there has been little work done in the area since then, with the exception of recent studies concerned with financial literacy costs and benefits (Commonwealth Bank Foundation 2005). Assessing the benefits (returns) of workplace training in general has received some attention (for example Moy and McDonald 2000), although the role of literacy and numeracy is often implied rather than explored in any detail. In contrast, there is a considerable body of relevant research emanating from the United States, Canada, the United Kingdom and some European countries. The release of data from the International Adult Literacy Survey (IALS) in the 1990s contributed to some of this research, as did policy developments for example, in the United Kingdom. The much greater use of IALS data in some other countries compared with Australia, seems to be related to a combination of factors in the overall policy and research environment for adult literacy and numeracy in each country.
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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 | 0.121 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.002 | 0.013 |
| Scholarly communication | 0.011 | 0.037 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".