Bibliographic record
Abstract
Une enquête menée en 2022 auprès de 3 742 étudiants et étudiantes de première année de notre université, en Belgique, questionne ces derniers sur leurs compétences numériques.Le questionnaire porte en partie sur les compétences du référentiel DigComp Citizen et sur des questions spécifiques de création de contenu.Des analyses statistiques descriptives, de distribution et de corrélation ont été réalisées.Environ 36 % des individus sondés se trouvent sous le seuil déterminé.L'absence de compétences numériques suffisantes serait corrélée avec un indice de risque (calculé avec 13 variables).Ceci permet de définir un indice de vulnérabilité numérique.Les étudiants et étudiantes en échec et qui ont un indice de vulnérabilité numérique élevé n'améliorent pas leurs compétences numériques durant leur expérience universitaire.Ceci serait un enjeu pour les services d'aide à la réussite.
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 | 0.010 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".