A Connected Generation? Digital Inequalities in Elementary and High School Students According to Age and Socioeconomic Level | Une génération connectée? Inégalités numériques chez les élèves du primaire et du secondaire selon l’âge et le milieu socioéconomique
Bibliographic record
Abstract
The objective of this article was to better understand the relationship between students’ age and socioeconomic level, and its influence on students’ digital uses. We conducted a quantitative study of 401 elementary and high school students in Quebec. Four independent variables were initially selected: two related to age (actual age and education level) and two others related to the socioeconomic environment (school poverty index and parents’ employment status). The dependent variable that represented students’ digital uses was the number of different technologies they used weekly. We conducted correlation tests followed by a linear regression analysis. Socioeconomic level appears to have a stronger influence on students’ digital uses compared to age, and explanations for this are proposed.L'objectif de cet article est de mieux comprendre la relation entre l’âge et le milieu socioéconomique des élèves dans leurs usages numériques. Nous avons mené une étude quantitative auprès de 401 élèves du primaire et du secondaire dans la région de Montréal. Quatre variables indépendantes ont été sélectionnées initialement, dont les deux premières renvoient à l'âge (l'âge et l'ordre d'enseignement) alors que les deux dernières renseignent sur le milieu socioéconomique (l'indice de défavorisation des écoles et la situation d'emploi des parents d'élèves). La variable dépendante permettant de rendre compte des usages numériques des élèves était le nombre de technologies qu'ils utilisent sur une base hebdomadaire. Nous avons procédé à une régression linéaire précédée de tests de corrélation. Il en ressort que le niveau socioéconomique semble influencer davantage les usages numériques des élèves que l'âge pour plusieurs raisons explorées dans cette recherche.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".