Undected Risk is Risky Business: Validating a Literacy Screening Battery for Children in Early French Immersion
Bibliographic record
Abstract
Universal and systematic literacy screening in the early years of elementary education allows for timely identification of- and subsequent intervention for- children who are at risk for developing reading difficulties. While several research-supported tools exist for literacy screening in English classrooms, screening tools developed for and normed on students in Canadian French immersion programs are lacking. This poses a barrier for the population of children learning French as a second language in our country, which accounts for a notable one in six students outside of Quebec. In the absence of universal screening, children who require intervention often slip through the cracks and go unidentified past the years in which targeted intervention is most effective, and children in French immersion are often advised to switch to English programs where there is more available support. Evidence-based screening tools that have undergone the appropriate validation processes play a crucial role in universal screening.In response to the need for effective screening tools for this population, we created the Test des Habiletés Fondamentales en Littératie (THaFeL): a screening battery consisting of letter naming, word reading, pseudoword reading, and oral reading fluency measures. The aim of the current study is to evaluate the validity and diagnostic accuracy of this tool. Results show good overall accuracy and provide support for the use of the THaFeL as a valid, effective tool for identifying at-risk readers in early French immersion, leading to appropriate and timely intervention. Limitations and future directions are discussed.
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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".