Adult Literacy and Life Skills Survey: Public Use Microdata File
Bibliographic record
Abstract
Governments and other stakeholders are increasingly interested in assessing the skills of their adult populations in order to monitor how well prepared they are for the challenges of the modern knowledge-based society. Adults are expected to use information in complex ways and to maintain and enhance their literacy skills to adapt to ever changing technologies. Literacy is important not only for personal development, but also for positive educational, social, and economic outcomes. Adult literacy, numeracy and problem-solving skills encompass a continuum of learning that enables individuals to achieve their goals, develop their knowledge and potential, and participate fully in their communities and society as a whole. Canada has been participating in adult skills assessment surveys for several decades. The surveys are repeated every ten years, with the first in the series taking place in 1994. First there was the International Adult Literacy Survey (IALS), then the International Adult Literacy and Skills Survey (IALSS) in 2003 and the Programme for the International Assessment of Adult Competencies (PIAAC) cycles 1 and 2 in 2012 and 2022 respectively. Each of these surveys builds on the concepts of the previous surveys. Over the years, the framework has broadened the definition of literacy to adapt it to the information age, notably by including reading skills in digital environments.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.040 | 0.034 |
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".