Name changing and gender
Bibliographic record
Abstract
Name changing is an under-researched topic in socio-onomastics. In this article, we extend knowledge of and understanding about gender and name changing by analysing ‘enrolled deed polls’, which people in the United Kingdom can use to change any part or all parts of their names. We examine which names are changed in relation to gender, including those we linked to transitions in gender identity. Our quantitative analyses of 10 665 enrolled deed polls for the period 1998–2019 shows that, over time, women have replaced men as the majority of applicants for name change and that, compared to men, women are more likely to make ‘surname only’ changes to their name. Among men applicants, there was an increase over time in changes made to first and middle names (a doubled figure in 2019 compared to 1998). Although case numbers are small, of the name changes we attributed to gender transition, the majority were changes made to the applicant’s first name and/or middle name. Our article concludes by reflecting on what our analysis of otherwise unexamined records of enrolled deed polls reveals about the (re)doing of gender identities through name changing in contemporary societies.
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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".