Bibliographic record
Abstract
It has been said that a language is a dialect with an army and a navy. By this criterion, Basque, Quechua, Navajo, Gullah, Yiddish, and Aramaic are not languages, while United States and Canadian English qualify as separate languages. During the Franco regime in Spain Catalan, Galician, and even Basque were declared to be dialects of Spanish. No linguist today would accept grouping Basque and Spanish as dialects of a single language, since they share no common genealogy and have zero mutual intelligibility. With the remaining Ibero-Romance offshoots, there is no ready algorithm to define the language-dialect dichotomy. And while most modern observers would classify Catalan and Castilian as distinct languages, the Aragonese and Asturian dialect clusters, which share many of the grammatical features of Catalan and which have their own literary corpora, have at times been classified as dialects of Spanish (e.g., by Entwistle 1936). Spanish and Portuguese—spoken in separate nations and enjoying long literary traditions—are invariably classified as distinct languages, although many of the differences are quite systematic and a high degree of mutual intelligibility exists between most varieties. An examination of various contact phenomena leads to the conclusion that closely related varieties such as the Spanish-Portuguese dyad do not fit easily into structural and sociolinguistic models designed for bilingual speech communities in which the languages are more distinct from one another. Nor does the continuum model used to depict the transition between basilectal and acrolectal varieties of a language account for the Spanish-Portuguese contact data. This in turn calls for additional research on the psycholinguistics and sociolinguistics of groups that learn or use closely related languages.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.009 | 0.009 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".