Non-gendered pronoun processing: an investigation of the gender non-specific third person singular pronoun ‘TA’ in Chinese
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study investigated the processing of the Chinese nongendered third-person singular pronoun, “TA,” in a series of self-paced reading experiments. We begin by investigating the perceived appropriateness of TA using a novel implementation of the modified maze task. We then contrasted reading latencies for TA and male- and female-gender pronouns in reference to antecedents with varying stereotypical gender (e.g., occupation terms) and definitional gender (e.g., kinship terms). In our analysis, we assessed several means of operationalizing stereotypical gender information. Optimal model performance was achieved with a continuous measure that accounted for individual differences in gender perception, suggesting the involvement of a probabilistic component. Results for reading latencies and perceived appropriateness of TA support previous findings from discourse analysis that TA is not entirely gender-neutral but rather has nuanced contexts of use in modern Chinese written discourse.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it