Roma Chumak-Horbatsch (2025): Multilingual Teaching: The Missing Piece. Bristol/Jackson: Multillingual Matters.
Bibliographic record
Abstract
Multilingual Teaching: The Missing Piece (Chumak-Horbatsch, 2025) offers a concise reflection on inclusive language education. The book’s central claim is that learners who already master the language of school should be explicitly included in multilingual education initiatives. Before examining the book’s contents, one conceptual stance deserves attention: the use of the word ‘multilingual’ in the title and throughout the book. There is general consensus that multilingualism refers to the presence of multiple languages within a space or community (Council of Europe, 2001). However, while the term multilingual is commonly used as an adjective to describe educational approaches in America and Canada, in Europe the term plurilingual is preferred, as the two concepts are not regarded as synonyms. ‘Plurilingual’ stands in contrast to ‘unilingual’. Unilingual pedagogies advocate for teaching languages in isolation, whereas plurilingual pedagogies recognise and use all the languages students know – both school and home/community languages – as resources for learning (de la Cruz, 2019). Chumak-Horbatsch’s ‘language-rich classrooms’ are multilingual in composition; however, her pedagogical objectives may be interpreted as either multilingual or plurilingual, depending on the reader’s background.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.033 | 0.019 |
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".