Bibliographic record
Abstract
Abstract The pronounsthey/them/theirare readily available with a singular interpretation as bound variables (Balhorn 2004, Bjorkman 2017). Referential interpretations are possible, but subject to pragmatic considerations and changes in progress (Bjorkman 2017, Conrod 2019, Konnelly and Cowper 2020). In a series of experiments, we tested differences between bound and referential singulartheyin acceptability and incremental processing, asking whether boundtheyis sensitive to the gender of its antecedent, as referentialtheyis (Doherty and Conklin 2017, Ackerman 2018, Ackerman et al. 2018, Conrod 2019). We found that bound singulartheyhas an advantage over referential singulartheyin acceptability, even when the antecedent is gendered. In processing, however, bound-variable singulartheyshowed a reading time advantage over referential singulartheyonly with gendered antecedents. We evaluate these results against existing formal linguistic theories of singulartheyimplemented within psycholinguistic models of pronoun processing. We submit that none of the theories fully captures the range of evidence we uncover, in particular the interaction between gender and quantification. We suggest a formal account that does: we propose, using representations from Kratzer (2009) and Sudo (2012), that gender and number features are differentially represented in referential versus binding dependencies. We speculate how this representational difference relates to the processing mechanisms of antecedent retrieval and to the limited processing advantage for bound singulartheythat we found.
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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 | 0.005 |
| 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 | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".