Expanding structures while reducing mappings: Morphosyntactic complexity in agglutinating heritage languages
Bibliographic record
Abstract
Research on heritage language grammars to date provides overwhelming supportfor the general stability of their syntactic systems, while the status of their morphology can vary considerably. In this chapter we offer remarks on the morphologicalcomplexity of agglutinating heritage languages, taking a closer look at a numberof phenomena in Labrador Inuttitut, Cherokee, and American Hungarian. Four important findings emerge from our review: First, these phenomena align with previously documented and observed patterns in heritage language morphology (Polinsky 2018, Putnam et al. 2021). Second, heritage language morphology maintainsa significant degree of complexity, even in languages found to be in a moribundstate (Bousquette & Putnam 2020). Third, adopting an exoskeletal approach to morphosyntactic decomposition and complexity (Lohndal & Putnam 2021), we observetrends towards larger syntactic structures (for lexicalization), and inversely a reduction in the inventory of exponency. Fourth, we observe a general trend in the“shrinking” of computational domains for lexicalization and movement operations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.041 | 0.003 |
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; both teacher heads agree on what is shown here.
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".