Are large surveys of adult literacy skills as comparable over time as we think?
Bibliographic record
Abstract
Abstract Recent literature shows that younger cohorts have lower levels of literacy ceteris paribus in Canada, the United States, Norway and other developed countries. Very few explanations are provided to justify the existence of this negative cohort effect. Yet this decline has serious implications for the economy, education system and society. In this paper, we focus on Canada and replicate the results published in the literature using the same methodology (synthetic cohorts). We use the same data from surveys of adult skills, namely the International Adult Literacy Survey (IALS), the Adult Literacy and Life Skills Survey (ALL) and the Survey of Adult Skills, a product of the OECD Programme for the International Assessment of Adult Competencies (PIAAC). Second, we conduct further analyses to better understand the effect of time on adults’ literacy skills. We then show that age has a negative effect on the literacy score, but also that a significant “period” effect (or rather a “survey” effect) can only be explained by a change in the instrument used to measure literacy skills from one survey to another. This article reveals that the negative cohort effect mentioned in the literature may be fallacious as it is exacerbated when the control for measuring instrument distortions is omitted. This paper contributes to advancing knowledge about the effect that age and cohort has on adult literacy levels in Canada. The results weaken the idea of a clear negative cohort effect; much of this effect would in fact be caused by a non-comparable methodology that systematically assigned individuals lower scores in the most recent survey cycles. These findings are important because they highlight the limitations of analyses that can be done with the synthetic cohort method using cross-sectional surveys on adult literacy skills not only in Canada, but also in all other countries where these surveys have been conducted.
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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 | 0.135 | 0.472 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".