Skills, Signals, and Labour market outcomes: An Analysis of the 2012 Longitudinal and International Study of Adults
Bibliographic record
Abstract
This report concludes that skills and credentials are both important predictors of an individual’s labour market outcomes. Literacy and numeracy skills are strong predictors of both income levels and employment status, whereas credentials appear to have a significant effect on earnings alone, the study found. According to the study, numeracy appears to be a stronger predictor of earnings and employment status than literacy skills. It found that an increase in numeracy proficiency from Level 1 to Level 2 as measured by the Programme for the International Assessment of Adult Competencies resulted in higher earnings, while only higher levels of literacy, Level 3 and above, boosted earnings. Similarly, increasing numeracy across any level improved the odds of an individual being employed full time, while only higher-level increases in literacy resulted in the same outcome. The authors suggest that students may wish to focus not only on fulfilling their degree and diploma requirements, but also taking advantage of additional opportunities to improve their literacy, numeracy and other skills such as verbal communication and teamwork. They note that these can be acquired in the classroom as well as through co-ops, internships and other work-integrated learning opportunities.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".