Comparing individual and collective management of referential choices in dialogue
Bibliographic record
Abstract
Past research shows that when a discourse referent is mentioned repeatedly, it is usually introduced with a full noun phrase and maintained with a reduced form such as a pronoun. Is this also the case in dialogue, where the same referent may be introduced by one person and maintained by another person? An experiment was conducted in which participants either told entire stories to each other or told stories together, thus enabling us to contrast situations in which characters were introduced and maintained by the same person (control condition) and situations in which the introduction and the maintaining of each character were performed by different people (alternating condition). Story complexity was also manipulated through the introduction of one or two characters in each story. We found that participants were less likely to use reduced forms to maintain referents in the alternating condition. The use of reduced forms also depended on the context in which the referent was maintained (in particular, first or second mention of a character) and on story complexity. These results shed light on how the pressure to signal understanding to one's conversational partner affects referential choices throughout the interaction.
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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 | 0.009 | 0.040 |
| 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 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".