Motion Events in L1 and L2 Mapudungun Narratives: Typology and Cross-Linguistic Influence
Bibliographic record
Abstract
We compare the motion lexicalization patterns produced by L1 and L2 speakers of Mapudungun, an indigenous minority language spoken in Chile and Argentina. According to previous descriptions, the patterns of motion expression in Mapudungun have some characteristics of an equipollently-framed language, which contrast with the usual motion expression in Spanish. The data comprise oral narratives of the picture storybook “Frog, where are you?”, collected from 10 Mapudungun native speakers and 9 Spanish native speakers who are late bilinguals of Mapudungun. We report the general results (comparison of total clauses, translational clauses, types, and tokens) and analyze three general conflation patterns: the encoding of the semantic components of Path and Manner, the conflation of various components into serial verb constructions, and the encoding of Ground. The results show that L2 speakers encoded a significantly lower proportion of Manner verbs and a higher proportion of Path verbs than L1 speakers, used a significantly less diverse inventory of Path and Manner verb types, a significantly lower number of motion serial verb constructions, and a significantly higher number of plus-Ground clauses than L1 speakers, suggesting cross-linguistic influence from Spanish.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".