Bibliographic record
Abstract
Literacy research reveals that early literacy and reading skills are related to and are strong predictors of later reading ability and success in school (Lonigan, Purpura, Wilson, Walker, & Clancy-Menchetti, 2013; Lonigan, Schatschneider, & Westberg 2008). Our competence in these skills affects us socially, emotionally and physically. In 2012, the Programme for the International Assessment of Adult Competencies (PIAAC) showed that 17% of Canadian working-age adults (16-65) have very poor literacy skills (Hayes, 2013; Statistics Canada, 2013). These individuals may be unable to, for example, determine the correct amount of medicine to give a child from information printed on the bottle. A staggering 32% of Canadian adults have poor literacy skills and can deal with materials and tasks that are simple, clearly laid out, and not too complex (Canadian Council on Learning, 2008a). This group of adults may have developed coping strategies to deal with daily routines and other literacy demands but they may have difficulty with novel tasks (Canadian Council on Learning, 2008a; Hayes, 2013). Although these are adult literacy levels, literacy development begins at birth, and so begin the trajectories of vulnerability for academic challenges. The time to prevent low literacy skills is in the early years (Carroll, Bowyer-Crane, Duff, Hulme, & Snowling, 2011).
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".