Bibliographic record
Abstract
In this article, we establish direct links between language policy on the one hand and assessment in multilingual contexts on the other hand. We illustrate the bi-directional relationship with the examples of the USA, Canada, and the Basque Country. That comparison is placed in the context of the changing views about the use of languages in education where a shift can be observed away from an emphasis on separating languages to approaches that more closely suit daily practices of multilinguals. This concerns a shift from language isolation policies in language teaching and assessment towards more holistic approaches that consider language-as-resource and promote the use of the whole linguistic repertoire. However, the implementation of programs based on holistic approaches is limited and application in language assessment modest. Traditions and monolingual ideologies do not give way easily. We show some examples of creative new ways to develop multilingual competence and cross-lingual skills. The assessment of interventions with a multilingual focus point to a potential increase in learning outcomes. Multilingualism is a point of departure because in today's schools, students who speak different languages share the same class, while at the same time learning English (and other languages). We conclude that holistic approaches in language education policy and multilingual assessment need to substitute more traditional approaches.
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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 | 0.025 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.024 |
| Scholarly communication | 0.014 | 0.013 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 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".