Test-Takers’ Background, Literacy Activities, and Views of the Ontario Secondary School Literacy Test
Bibliographic record
Abstract
This study examined the relationships among students’ background information and their in-school and after-school literacy activities, as well as the relationships between students’ background and their views of the Ontario Secondary School Literacy Test (OSSLT). The results showed that students’ literacy activities could be grouped into three types: e-literacy, traditional literacy, and creative literacy. Furthermore, results showed that categorization of literacy activities depended on whether the activities were conducted in English or in another language. Gender predicted certain types of literacy activities. Compared with English-as-a-first-language (L1) students, English-as-a-second-language (L2) students’ background influenced more of their views of the test.Cette étude a porté sur les rapports, d’une part, entre les antécédents des élèves et leurs activités scolaires et parascolaires en matière d’alphabétisation et, d’autre part, entre ces renseignements généraux et la perception qu’ont les élèves du test provincial de compétence linguistique de l’Ontario (TPCL). D’après les résultats, il est possible de regrouper les activités d’alphabétisation des élèves en trois catégories : l’alphabétisation électronique, l’alphabétisation traditionnelle et l’alphabétisation créative. De plus, les résultats indiquent que la catégorisation des activités d’alphabétisation dépendait de la langue dans laquelle se déroulaient les activités (anglais ou autre). Le genre constituait une variable prédictive de certains types de ces activités. Les antécédents des élèves dont l’anglais était la langue seconde influençaient plus leur perception du TPCL que ceux des élèves pour qui l’anglais était la langue maternelle.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.040 | 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 teacher head, 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".