Bibliographic record
Abstract
This article explores the linguistic identity of young multilingual learners through the use of a Language Portrait Silhouette. Examples from a research study of children aged 6–8 years in a German bilingual program in Canada provide teachers with an understanding that linguistic identity comprises expertise, affiliation, and inheritance. This article also provides additional concrete examples of how teachers can openly reference linguistic identity with students and help children to see stronger connections between home and school learning. The validation and understanding of linguistic identity is beneficial to young children’s emotional, social, and educational development.Cet article examine l’identité linguistique de jeunes apprenants plurilingues par l’emploi d’un portrait silhouette langagière (Language Portrait Silhouette). Quelques exemples d’une recherche portant sur des élèves âgés de 6 à 8 ans dans un programme allemand bilingue au Canada démontrent aux enseignants que l’identité linguistique comprend les aspects l’expertise, l’affiliation et l’héritage. Cet article offre également des exemples concrets sur diverses façons de parlerouvertement d’identité linguistique avec les élèves et de les aider à établir des liensplus solides entre ce qu’ils apprennent à l’école et à la maison. Le fait de valider et de comprendre l’identité linguistique favorise le développement affectif, social et éducationnel des jeunes enfants.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".