Rethinking heritage language education
Bibliographic record
Abstract
Acknowledgements Notes on contributors Series editors' preface Introduction Peter Pericles 1. Mainstreaming plurilingualism - restructuring heritage language provision in schools Jim Cummings 2. Teaching heritage language learners - a study of programme profiles, practices and needs Maria Carreira 3. Rethinking heritage languages - ideologies, identities, practices and priorities in Canada and China Patricia A. Duff and Duanduan Li 4. The place of heritage languages in language education in Australia - a conceptual challenge Angela Scarino 5. Courses in the language and culture of origin and their impact on youth development in cultural transition - a study amongst immigrant and dual-heritage youth in Switzerland Elena Makarova 6. Language policies in the context of Australian civic pluralism Eugenia Arvanitis, Mary Kalantzis and Bill Cope 7. Communities taking the lead - mapping heritage language education assets Themistoklis Aravossitas 8. Overcoming challenges of language choice in heritage language development amongst multilingual immigrant families James C. Kigamwa 9. The impact of the CEFR on Canada's linguistic plurality - a space for heritage languages? Enrica Piccardo 10. Strengthening our teacher community - consolidating a 'signature pedagogy' for the teaching of Spanish as heritage language Maria Luisa Parra 11. Canada's 'other' language - the role of non-official languages in ethnic persistence Jack Jedwab 12. Rethinking heritage language in a critical pedagogy framework Panayota Gounari.
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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.011 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.034 | 0.005 |
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".