A multi-generational analysis of heritage language complexity
Bibliographic record
Abstract
Heritage languages are often of interest because of the ways in which they differfrom the relevant baseline. Many conceive of these differences as a process of simplification: a loss of inflectional morphology, less lexical richness, etc. Inspired byfindings in the literature that decreased complexity in one area of a language maylead to increased complexity in another, we take up the question of whether thechanges during the development of heritage languages involve a general simplification, or whether complexity trades off in heritage languages as it does in otherlanguages: as speakers rely less on word-internal structure, word order mattersmore, and vice versa. We apply information-theoretic measures of complexity inthe domain of word structure (i.e., morphology) and word order (i.e., syntax) to sixlanguages from the Heritage Language Documentation Corpus (Nagy 2011), whichincludes multiple generations of heritage languages and homeland comparators.Our results show partial support for complexity trade-offs in heritage languages,such that as the generations progress, word-structure complexity decreases whileword-order complexity increases.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
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".