Referring in dialogical narratives
Bibliographic record
Abstract
Abstract In a recent overview of the literature on spontaneous and experimentally-produced speech, Allen, Hughes, and Skarabela (2015) identified many discourse-pragmatic factors that affect the use of referring expressions. In this chapter, we first assess the individual effects and the relative importance of four factors (i.e., position of the referring expression in the referential chain and its syntactic function, the referent’s characteristics – primacy and/or animacy – and the chronological age) in a narrative dialogue between a mother and her child. Second, we describe the joint impact of these factors on the use of nouns and third-person pronouns. A total of 30 typically-developing French-speaking children aged 4 to 7 years participated with their mother in a joint storytelling. Our results corroborate those found in the literature on the factors affecting young children’s use of referring expressions. Furthermore, they show a complex network of relations between the factors, that interestingly, was not the same for nouns and third-person pronouns.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".