Bibliographic record
Abstract
nderstanding and using written language is a highly sophisticated skill, and humans are the only animals able to do so to any degree of complexity.Most of us can process series of symbols with near-infinite possible combinations, and understand what they signify, all in milliseconds.However, humans have only been reading for around 5,000 years, which is not enough time for this skill to evolve -which means we read using parts of our brains that likely evolved for something else.Understanding how we read is important, particularly to provide solutions for those with lower reading and writing abilities."Literacy skills have a profound impact on a person's life," says Dr Jacqueline Cummine, a professor at the Faculty of Rehabilitation Medicine -Communication Science and Disorders at the University of Alberta."Even subtle Reading and writing are essential skills for modern life, but how often do you think about how your brain processes written information?Based at the University of Alberta in Canada, Professor Jacqueline Cummine is helping decode how we read -in particular, the important role of our senses -and using these findings to help people who struggle with literacy skills.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".