Literacy and labour market outcomes: self-assessment versus test score measures
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
This paper looks at the determinants of literacy and the relation between literacy and labour market outcomes while focusing on comparisons of self-assessment versus test score measures of literacy. The test score measure performs considerably better than the self-assessments when literacy is treated as an outcome variable in terms of the overall fit of the model and the specific coefficient estimates, with the self-assessments sometimes actually generating wrongly signed parameters. The test score measure also performs much better as an explanatory variable in the employment models, with the self-assessment variable generating significant underestimates of the effects of literacy on the probability of being employed. Finally, the test score is also superior in the income models, although the self-assessment measure is at least a reasonably good performer in this regard, suggesting that the main results reported in much of the existing literature (based on such measures) should perhaps be taken as good representations of the true underlying relationships.
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) | 0.001 | 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) | 0.000 | 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