Researching Literacy and Numeracy Costs and Benefits: What is possible
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.005 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it