Bibliographic record
Abstract
The CLLRNet is a member of the Networks of Centres of Excellence and is funded by the Canadian Government in the Medical, Natural, Engineering, and Social Sciences.Since its establishment in the fall of 2000, CLLRNet has grown to include 120 researchers and more than 175 students from 11 disciplines at 29 institutions across Canada.The vision of CLLRNet is to improve language and literacy skills in Canadian children, enabling them to contribute more effectively to the social and economic life of their communities.This vision beckons language and literacy educators to help understand and address these challenges.The research projects within CLLRNet are organized around five integrated themes: (1) biological factors underlying the development of language and literacy skills; (2) sensory processes and environment; (3) language development; (4) literacy acquisition and development; and (5) social, economic, and program influences (family, schools, and communities).Although several major studies are presently being conducted at CLLRNet, this report focuses on three studies.1. Across themes 1 to 4: The use of magnetic resonance imaging (MRI) to investigate the neurodevelopment of children as ground-breaking research.Reading is a complex cognitive skill that requires the coordination of multiple regions of the brain.Although functional activation studies highlight the cortical brain regions associated with cognitive tasks such as reading, they do not directly address the underlying neural connections necessary for efficient performance of this task.Adults with reading disabilities have demonstrated lower white matter connectivity, but it is not known whether this relationship between neuronal wiring and reading performance is also evident in younger readers.Thirty-two children between the ages of 9 and 12 years completed the Word Identification subtest of the Woodcock Reading Mastery Test with scores ranging from 72 to 129 and underwent a noninvasive MRI brain scan.An index of brain connectivity derived from diffusion tensor MRI (fractional anisotropy) was correlated, using SPM software, with reading ability.Preliminary findings suggested that there were correlations with regional brain structures over a wide range of reading ability even within a population of readers not evidencing problems in learning to read.A follow-up study on a well-defined
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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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.016 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.183 | 0.063 |
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".