Singular <i>they</i> in context
Bibliographic record
Abstract
There is a growing experimental and theoretical literature on singular they, much of it focusing on the nature of the antecedents it takes (Foertsch & Gernsbacher 1997; Bjorkman 2017; Doherty & Conklin 2017; Prasad 2017; Ackerman et al. 2018; Ackerman 2018a; Ackerman 2018b; Conrod 2018; Ackerman 2019; Camilliere et al. 2019; Conrod 2019; Konnelly & Cowper 2020). We conducted two experiments which, in contrast to earlier studies, manipulated whether the gender of the referent of singular they is known to the discourse participants and whether there is a linguistic antecedent for singular they. We found that the presence of an antecedent ameliorates the acceptability of singular they—even in a context where the gender of the referent may be known to the hearer. We interpret this novel finding as revealing how a linguistic antecedent can signal the irrelevance of gender in a discourse and thereby licenses singular they. We also find a trend, inversely correlated with age, toward higher acceptability of even deictic singular they in gender known contexts, partially bearing out findings in Bjorkman (2017), Conrod (2019), and Konnelly & Cowper (2020) about innovative users of singular they.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.032 | 0.006 |
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".