Bibliographic record
Abstract
Abstract The papers in this special issue address themes fromThey, Hirself, Em, and YOU 2019(THEY 2019), a conference that brought together researchers working on topics relating to nonbinary gender in language, particularly in pronouns. The papers collected in this special issue provide an overview of the current state of research and practice on nonbinary pronouns as they are currently used in English, as well as connecting the current practices in English to nonbinary pronouns in other languages. There are two sections. In the first section are five traditional academic articles on non-binary language and pronouns; the second section features three short technical articles that raise practical and/or pedagogical issues related to non-binary pronouns from a scholarly perspective. Authors in this volume investigate these topics not only for the advancement of linguistic scholarship, but also to make that scholarship visible to other fields and for broader advocacy.
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.
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 | 0.033 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.039 |
| Scholarly communication | 0.028 | 0.027 |
| Open science | 0.003 | 0.019 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.014 | 0.005 |
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".