Addressee Identity and Morphosyntactic Processing in Basque Allocutive Agreement
Bibliographic record
Abstract
Information about interlocutor identity is pragmatic in nature and has traditionally been distinguished from explicitly coded linguistic information, including mophosyntax. Study of speaker identity in language processing has questioned this distinction, but addressee identity has been less considered. We used Basque to explore how addressee identity is processed during morphosyntactic analysis. In the familiar register hika, Basque has obligatory allocutive agreement, where verbal morphology represents the gender of a non-argument addressee. We manipulated the gender of the allocutive verb and the congruence of addressee gender in conversations between two interlocutors. Items with person agreement manipulations were included as a control comparison. Basque speakers familiar with hika completed speeded acceptability judgments and unspeeded, offline naturalness ratings for each conversation. Results showed a main effect of addressee identity congruence for naturalness ratings, but there was no main effect for addressee identity congruence for reaction times or accuracy in the acceptability judgment. Interactions and correlations with biographical data showed that the effect of congruence was modulated by the gender of the allocutive verb and that hika proficiency was related to participants’ performance for the acceptability judgment. These results show an interaction between morphosyntactic and pragmatic information and are the first experimental data of allocutive processing. In comparison, clear effects were seen for the person agreement condition, indicating that person disagreement is more disruptive to processing than addressee identity incongruence. This study has implications for investigation of the role of extralinguistic information in morphosyntactic processing, and suggests that not all such information plays an equal role.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".