ADULT BASIC SKILLS AND DIGITAL TECHNOLOGY: RESEARCH FROM THE UK
Bibliographic record
Abstract
Lack of basic skills has been seen as a key factor in disadvantage, high levels of unemployment and social exclusion. This linkage has been demonstrated across a number of Member States within the E.U. (BSA 1999) and also worldwide. In an international study of adult functional literacy (International Adult Literacy Survey- IALS), carried out by the Organisation of Economic Cooperation and Development (OECD 1995 and 1997), substantial variation was shown in literacy and numeracy levels; Scandinavian countries showed small proportions of adults operating at the lowest levels (e.g. 7 % in Sweden) whilst a number of English speaking countries such as the UK, Australia, Canada, and the USA showed much higher proportions (over 20 % in some cases). Total illiteracy is rare within the UK, but one in sixteen adults if shown a poster advertising a concert being held at a specific place cannot identify where the concert is being held, and one in four adults cannot calculate the change they should get out of £2 when they buy three articles of value 45p, 45p and 68p. Within the UK, it is estimated that maybe some 7,000,000 adults are operating at low levels of literacy and numeracy. The impact on personal economic prospects and the
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 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".