Bibliographic record
Abstract
Why worry about bilingualism? The first question that needs to be asked in a book like this is why this chapter is here at all. How did it come to pass that a concept like “bilingualism” got constituted as an area of enquiry for ethnolinguistics? I will begin here with a consideration of that question as one that is fundamentally about language ideologies, and then go on in the rest of the chapter to explore some of the specific questions that flowed, in my view necessarily, from an understanding of languages as being whole, bounded objects tied to whole bounded social and political units like ethnic groups, nations or states. Bilingualism (a term I will use here to cover multilingualism as well) is an affront to this idea, or at best a puzzle needing to be solved. As a result, academic work on the subject has tended to focus on explorations of the way bilingualism tests our ideas either of language or of social and political categories. One set of questions addresses whether or not bilingualism challenges linguistic theories linked to the idea of language as autonomous and whole; another examines the relationship between bilingualism and the construction of categories like ethnicity, or the nation (or the nation-State), understood as homogeneous and bounded entities, as well as with related categories or concepts, such as community or identity, all of which are central to ethnolinguistic enquiry.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.023 | 0.004 |
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".