Information Analyses (070) Numerical/Quantitative Data
Bibliographic record
Abstract
This study develops a literacy profile of Ontario's youth (ages 17-25) using data from the International Adult Literacy Survey (IALS). Following an introductory section, Section 2 provides a background on the IALS database and discusses key methodological issues. Section 3 presents an overview of basic literacy statistics. Section 4 compares results across four Canadian regions and across different countries. Section 5 explores differences in literacy among selected youth characteristics. Section 6 looks into the impact of extracurricular activities on the literacy levels of Ontario's youth. Section 7 addresses consequences of low literacy among youth. Section 8 identifies the main conclusions, including the following: (1) Ontario youth have better literacy skills than older Ontarians; (2) the rate of Ontario youth who exceed level 2 in document literacy is about the same as the national average; (3) relative to the national average, Ontario's youth skills are weaker in prose and quantitative literacy; (4) the strongest determinant of youth literacy is the individual's level of education, the second strongest is the mother's education; (5) activities with the strongest positive effect on the document literacy score are attending or participating in sports, using public libraries, taking courses, attending movies, plays, or concerts at least monthly, and limiting time spent watching television; and (6) literacy has an economic payoff. (Data tables are appended.) (YLB) Reproductions supplied by EDRS are the best that can be made from the original document. 1 z O acy On to
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 | 0.010 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.016 |
| 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) | 0.083 | 0.017 |
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".