Book Review: Heritage Language Policies around the World. (2018). Corinne A. Seals and Sheena Shah (Eds.) New York, NY: Routledge
Bibliographic record
Abstract
In this trailblazing volume, editors Corinne A. Seals and Sheena Shah join a unique group of authors with wide-ranging expertise in heritage languages (HLs) to examine the visibility, status, policies, and efforts pertaining to heritage languages in 15 countries spanning five regions (Americas, Europe, Africa, Asia, Australasia).Each chapter focuses on up to five minority/heritage languages and addresses, to varying degrees, these core questions:• Are heritage languages included or excluded from the national language policy discourse?• What are the successes and shortcomings of efforts to establish heritage language policies?• What is the definition of "heritage language" in official usage by the local/regional government and stakeholders?• How are these language policies perceived by the actual heritage language communities?The perennial question of how to define HLs and their speakers is central to Seals' and Shaw's ambitious global project.Following Fishman (1999, 2001) (who wrote in the U.S. context), their expanded definition of HLs includes Indigenous, colonial, and immigrant languages, and emphasizes HL speakers' agency in determining whether they identify as heritage speakers (Hornberger & Wang, 2008).Having published and researched extensively on HLs, endangered, and minoritized languages, Seals and Shaw explain their decision to include those three groups of minoritized languages in their definition to recognize "that languages do not have to be either one thing or the other [e.g., "immigrant" or "heritage"]; they can be both….and often [are], both at once" (p.3).Indeed, as they point out, 10 of the 15 chapters in this book focus on Indigenous languages (e.g., Indigenous languages in Canada, Irish Gaelic in Ireland, Amazigh in Morocco, Jujueo in South Korea, Te Reo Maori in New Zealand).
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.054 | 0.030 |
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".