The adoption of inclusive language in abortion related activism
Bibliographic record
Abstract
The purpose of this study is to produce accessible knowledge about strategies for implementing gender-inclusive terminology within pro-choice activism in Canada and understand resistance to this implementation. Traditionally, abortion and reproductive healthcare has used ‘woman’ to indicate everyone who can get pregnant, which excludes trans, non-binary, and other gender diverse people who can get pregnant. The shift to inclusive language, as opposed to gendered language, is essential in abortion related activism. Using language such as pregnant person instead of woman is also more accurate as not all women have the capacity to get pregnant, and not all people who get pregnant are women. Neither the need to access abortion care, nor the need to advocate for it, is limited to women. I conducted interviews with people from a variety of abortion related activism groups in Canada to learn about their experience with language changes and what language they use. Changing language used in abortion related activism is an important part of changing language in reproductive healthcare overall. There is significant overlap between the people and resources of activist groups and care providers in reproductive healthcare. This makes implementing inclusive language in all areas of reproductive healthcare and related activism essential to ensuring that everyone who has the capacity for pregnancy can access care in a respectful and affirming environment. My interviews showed that organizations who changed their language to be inclusive saw little to no backlash from transphobic groups or community members. Organizations which began with, and continue to use, inclusive language still encounter difficulty in the gendered nature of many available educational and informational resources. Overall, those most likely to notice the use of inclusive language were trans and non-binary people who were excluded by the use of gendered language in the past. There was no evidence of anyone feeling excluded by the use of inclusive language. This research and the documentation used to conduct it will be made publicly available to help with future work and language changes.
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 | 0.022 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.033 | 0.068 |
| Scholarly communication | 0.014 | 0.006 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".