“Very easy, it’s an English class, therefore they should not rely on a French text”
Bibliographic record
Abstract
Abstract In the context of additional language (AL) learning, teachers need to focus on the development of language proficiency as well as on high-level literacy skills, for example, comprehending and evaluating information and creating new meaning. From a plurilingual perspective, AL learners’ first language (L1) is conceputalised as a useful tool in the development of target language proficiency; however, limited information exists concerning AL teachers’ beliefs towards the use of the L1 for high-level literacy instruction despite its potential utility for complex skill development. The aim of the present exploratory study was to examine the beliefs of in-service teachers of English as an additional language (EAL) working in the Quebec primary and secondary school system in francophone Canada regarding plurilingual approaches for classroom literacy practices and to uncover the factors that influence their beliefs. An online survey was distributed to in-service EAL teachers (N = 57) working in the province of Quebec, Canada. Findings suggest that teachers believe that it is most beneficial to adopt a monolingual lens to literacy instruction, a belief that denies learners’ use of their L1 and this, in a bilingual country. Implications for teacher education programs that challenge a monolingual lens are explored.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.028 | 0.007 |
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".