Bound Variable Singular They Is Underspecified: The Case of All vs. Every
Bibliographic record
Abstract
The goal of this article is to investigate the factors that affect the acceptability and processing of they. Previous research has sought to determine whether there are acceptability and processing differences between they/themselves with plural vs. singular antecedents, with mixed results. The studies reported here address this question using bound variable singular they (e.g., Every customer claimed that they were first in line). We asked whether bound singular they is sensitive to both the morphological number and the semantic distributivity of the binding quantifier phrase. We contrasted morphologically singular quantified antecedents (every and each) with plural quantified antecedents (all). Instead of finding an effect of number, we found an effect of semantic distributivity in acceptability, with bound singular they demonstrating a cline of preference toward more distributive antecedents. Neither number nor distributivity, however, registered as an effect on reading times. Rather, for all types of quantified antecedents, encountering a pronoun like he or she rather than they registered a processing delay, in contrast to non-quantified antecedents. Our results are most fully compatible with the view that they is underspecified for number properties.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".