Bibliographic record
Abstract
How much of the economic benefit of an education can be attributed to literacy skills? Using Canadian data from the International Adult Literacy Survey (IALS), this paper examines the validity of averaging literacy scores across jurisdictions. It also sets bounds on the proportion of the individual benefit of education that can be explained by level of literacy. Osberg focuses on the methodology behind measures of skills such as literacy and, in so doing, opens an important methodological debate. Direct measures of skill attainment, like the IALS, are used to assess the importance of educational outcome skills—in this case, literacy—in determining labour market outcomes, such as earnings. Policy makers also use them to direct resources most efficiently. These measures of skill are the product of complex statistical procedures. This paper compares the strength of IALS measures with other approaches to estimating the impact of literacy on individual earnings. Osberg shows that for men employed full time and full year, for example, literacy accounts for about 30% of the economic return from education. Whatever way the literacy score is stretched for the full-time, full-year work force, it is always statistically significant. Yet the contribution of literacy can explain 40% to 45% of this return when the question is turned around to ask: what is the maximum fraction of the economic benefit of an education that can be explained by the inclusion of measured literacy skills?
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 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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".