SOURCES DE MATÉRIEL EN FRANÇAIS POUR L’ÉLABORATION D’ÉPREUVES DE COMPÉTENCES EN LECTURE ET EN ÉCRITURE
Bibliographic record
Abstract
L’élaboration d’une épreuve de competences en lecture ou en écriture présente de nombreux défis. On doit assurer la représentativité des items par rapport au concept ciblé et prendre en compte les variables linguistiques susceptibles d’influencer le comportement mesuré. Les informations afferents à ces variables linguistiques proviennent typiquement des dictionnaires spécialises ou des etudes normatives sur la langue. Le but du present article est de rapporter les résultats d’une recension systématique des sources de materiel en français pouvant server à l’élaboration d’épreuves de competences en lecture et en écriture. Ce compte rendu est organize selon une taxinomie hiérarchique dans laquelle nous opérons une différenciation progressive entre les unites segmentales de la langue, des unites supralexicales aux unites infralexicales. Les variables pertinentes sont définies et les sources de matériels ou de données normatives sont présentées. Mots clés : évaluation des compétences en lecture et en écriture, ressources pour la construction des épreuves en français, données normatives sur le lexique du français The development of tests for the assessment of reading or writing skills always is a challenging task. A representative sample of items for the theoretical construct of interest must be selected and the linguistic variables likely to influence the behavior being measured must be taken into account. The most useful information for this purpose is found in specialized dictionaries and normative data studies. The goal of the present article is to systematically review the sources of material in French that can serve in the assessment of reading and writing skills. This review is broken down into the segmental units of the French language, from supra‐lexical to sub‐lexical units. The relevant variables are defined and the sources of material and normative data are presented. Key words: assessment of reading and writing skills, resources for test construction in French, normative data on the French lexicon
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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 | 0.008 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 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".