The Monolingual Bias in Bilingualism Research, or: Why Bilingual Talk is (Still) a Challenge For Linguistics
Bibliographic record
Abstract
For a long time, linguists found it difficult to account for the use of two or more ‘languages’ within one utterance by the same speaker. It was acknowledged of course (from the nineteenth century onwards, at the latest) that languages can ‘borrow’ structures from other languages without ever returning them to the ‘owners’ (to stick to this somewhat problematic metaphorical field). No doubt languages such as German or, even more so, English had massively copied lexical and — to a lesser extent — grammatical elements (above all derivational affixes) from other languages, such as Latin or French. However, these borrowings were exclusively analysed post factum, i.e. after they had become fully incorporated into the borrowing language. Few linguists were interested in languages whose status was unstable and ambiguous; among them was the Austrian Hugo Schuchardt who investigated ‘mixed’ languages such as creoles and Romani varieties as early as 1884 and came to the conclusion ‘dass eine Sprache A ganz allmählich, durch fortgesetzte Mischung, in eine von ihr sehr verschiedene B übergehen kann’ [‘that a language A can transform slowly but steadily, by constant mixture, into a language B which is very different from it’]. He continued on a somewhat fatalistic note: ‘Für die Beantwortung der Frage aber ob sie an einem bestimmten Entwickelungspunkt noch A oder schon B zu nennen sind, fehlte es uns gänzlich an Kriterien’ [‘However, we would lack all criteria to answer the question whether they can still be called still A or already B at a certain point of development’] (1884: 10). These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.021 |
| Scholarly communication | 0.007 | 0.017 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".