On the use of names and example sentences in the linguistics classroom
Bibliographic record
Abstract
Abstract In addition to representing a main source of data in linguistic research, example sentences are a core vehicle for linguists in teaching a wide range of phenomena to our students. However, the content of these sentences often reflects the biases of the researchers who construct them: referents are typically given Anglocentric proper names like John and Mary, reflecting (at least implicitly) dominant white culture and conformity to heteronormative gender roles. To support linguists in shifting these practices, we present the Diverse Names Database, a database of 78 names from a variety of languages and cultures, confirmed with native speakers. We outline the goals for the project, introduce our process of developing and adjusting the design, and present some additional issues and reflections for consideration, such as how to use the database as one component of an affirming, anti-racist, and gender-equitable linguistics pedagogy. We aim to generate meta-level discussions about disciplinary conventions and canons, and to challenge the idea that underlying linguistic structures are, or should be, the only things of relevance when constructing example sentences. How we teach linguistics is part of how we practise it, and how we do both matters to the composition and direction of the field.
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 | 0.043 | 0.120 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.010 | 0.016 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".