Predictors of literacy in adulthood: Evidence from 33 countries
Bibliographic record
Abstract
What makes a literate person? What leads to literacy gains and losses within and between individuals and countries? This paper provides new evidence that helps answer these questions. The present comparative analysis of literacy is based on large representative samples from the Survey of Adult Skills conducted in 33 countries, with 25-65 year old participants. We provide, for the first time, estimates of relative importance for a comprehensive set of experiential factors, motivations, incentives, parental influence, demands of workplace, and other predictors of influence. We sketch a configuration of factors that predicts an "ideal" reader, i.e., the optimal literacy performance. Moreover, we discover a pivotal role of the age effect in predicting variability between countries. Countries with the highest literacy scores are the ones where literacy decreases with age the most strongly. We discuss this finding against current accounts of aging effects, cohort effects and others. Finally, we provide methodological recommendations for experimental studies of aging in cognitive tasks like reading.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".