Bibliographic record
Abstract
Much of the research on minority and understudied languages focuses on language policy and politics (maintenance, endangerment, and revitalization). However, recently, a spotlight has been placed on the special theoretical import of these languages, and the role they play in shaping our understanding of the language faculty and the linguistic landscape.In recent decades, and especially recently, researchers are appreciating just how many understudied languages exist, this includes many endangered and/or minoritized bona fide varieties/languages (or clusters of thereof): Lombard, Sicilian, Rheto-Romance Judeo-Roman, Arpitan, Occitan, Istro-Romanian, but also creoles or creolized languages, and colonial languages: Michif, Papiamentu, Palenquero, Louisiana Cajun French and linguistic codes that are so minoritized, they are barely considered ‘varieties’ at all: pidgins, mixed and interlanguages (Llanito, Portuñol, Chavacano), and often acategorical and idiosyncratic (but still linguistically revealing) speech of heritage speakers.A great many of these minority languages are Romance in origin and since there has been active, intensive and systematic research on the major Romance varieties for over a hundred years, this provides a backdrop from which to ask highly specific, micro-dialectal, micro-parametric, formal questions that are relevant to linguistic theory.This special issue creates a space to further champion the theoretical relevance of the morpho-syntax, morphology and phonology of minority and minoritized linguistic languages of Romance origin, which are understudied, both inside and outside of Europe.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.018 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".