Pronoun interpretation in Italian: assessing the effects of prosody
Bibliographic record
Abstract
In this paper, we offer a prosodic account to supplement some well-known findings relating to choice of antecedents for pronouns in Italian. We argue that methodologies previously used to assess pronoun interpretation are flawed in that they rely only on written language to assess interpretation. In biclausal sentences like (1a), null pronouns are preferred when the antecedent is the discourse topic and subject of a higher clause; otherwise, overt pronouns are preferred. Sorace and Filiaci (2006) and Belletti et al. (2007) report that second language (L2) speakers of Italian overuse overt pronouns in contexts where null pronouns would be appropriate; they attribute this overuse to problems at the syntax-discourse interface (a failure to fully appreciate the discourse requirements on overt pronouns) and/or to processing problems relating to the Position of Antecedent Strategy (PAS) proposed by Carminati (2002). In addition to the behaviour of the L2ers with respect to overt pronouns, there are some puzzling results in this literature: both native speakers and L2ers fail to perform as expected with null pronouns, allowing them to take object antecedents about 50% of the time.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".