Pronouns beyond phi-features: the speaker–addressee relation in Japanese pronouns and its implications for formal pronouns
Bibliographic record
Abstract
Abstract Greenberg’s Universal 42 states that all languages have pronominal categories involving at least three persons and two numbers. However, this characterization fails to capture the properties of pronouns in Japanese, which are not bundles of person, number and gender features (so-called phi-features ); rather, they contain sociolinguistic information about the interlocutors. We propose that these properties are structurally determined. Following Ritter and Wiltschko, we assume that the highest layer of structure in nominals is interactional structure. As for phi-features, we adopt the standard assumption that they are represented internal to the determiner phrase (DP). We propose that the distinctive properties of Japanese pronouns follow from the hypothesis that they spell out elements of the interactional structure and not the DP. We show that the lack of phi-features in Japanese pronouns correlates with other properties of this language’s grammar. Support for this analysis comes from languages where pronouns with phi-features can optionally be used to encode formality (e.g. German and French). We propose that in these languages, formal pronouns originate within the DP but are interpreted in the interactional structure. Finally, we suggest that this analysis may extend to imposters and vocatives in that they may also be interpreted in the interactional structure.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".