Version Française du Test Numeracy Screener (NS-f), un Outil de Dépistage des Difficultés de Traitement du Nombre et des Quantités
Bibliographic record
Abstract
The Numeracy Screener was developed by Dr. Nadia Nosworthy and Dr. Daniel Ansari at the Numerical Cognition Laboratory at Western University (Canada). This test is a brief (2 - 4minutes) screening tool of basic number and numerosity processing skills for children ages 5to 9 years. The goal of the present study was to establish a French version of the Numeracy Screener (NS-f), for which the translation is valid. For that purpose, a method of back-translation was used. The results showed a good correspondence (near 80 % adequation)between the original version and both retranslated versions. It thus suggests that the French version is valid. Preliminary results were also acquired and showed a coherence between scores on NS-f and the individual profiles of children. This study thus provided empirical support towards the standardization of the NS-f. This will allow, in the future, for further validity and reliability testing of the NS-f and the development of standardized norms with a tool adapted for the French-speaking population
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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 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) | 0.007 | 0.002 |
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".