Groupements catégoriels et définitions des difficultés en lecture et en orthographe
Bibliographic record
Abstract
Cet article établit un point d’une recherche sur la relation, d’une part des difficultés lexiques ou orthographiques et, d’autre part, des facteurs qui peuvent en être à l’origine. Les difficultés en lecture sont classées en trois grandes catégories : 1) les erreurs de son; 2) les erreurs de forme; 3) les autres types d’erreurs. La classification des difficultés en orthographe se fait aussi sous trois catégories : 1) les erreurs de son; 2) les erreurs de grammaire; 3) les erreurs d’usage. Chacune de ces catégories se subdivise en sous-groupes. Ces classifications sont faites pour aider les spécialistes en troubles d’apprentissage ainsi que les enseignants qui doivent intervenir auprès des élèves qui ont des difficultés d’apprentissage en lecture et en orthographe.
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 | 0.006 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.017 | 0.009 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".