Ageing and Literacy Skills: Evidence from Canada, Norway and the United States
Bibliographic record
Abstract
We study the relationship between age and literacy skills in Canada, Norway and the U.S. &- countries that represent a wide range of literacy outcomes - using data from the 1994 and 2003 International Adult Literacy Surveys. In cross-sectional data there is a weak negative partial relationship between literacy skills and age. However, this relationship could reflect some combination of age and cohort effects. In order to identify age effects, we use the 1994 and 2003 surveys to create synthetic cohorts. Our analysis shows that the modest negative slope of the literacy-age profile in cross-sectional data arises from offsetting ageing and cohort effects. Individuals from a given birth cohort lose literacy skills after they leave school at a rate greater than indicated by cross-sectional estimates. At the same time, more recent birth cohorts have lower levels of literacy. These results suggest a pervasive tendency for literacy skills to decline over time and that these countries are doing a poorer job of educating successive generations. All three countries show similar patterns of skill loss with age, as well as declining literacy across successive cohorts. The countries differ, however, in the part of the skill distribution where falling skills are most evident. In Canada the cross-cohort declines are especially large at the top of the skill distribution. In Norway declining skills across cohorts are more prevalent at the bottom of the distribution. In the U.S. the decline in literacy skills over time is most pronounced in the middle of the distribution.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.010 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".