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Record W6886070870 · doi:10.14288/1.0438622

The adoption of inclusive language in abortion related activism

2024· article· en· W6886070870 on OpenAlexaboutno aff

Bibliographic record

VenuecIRcle (University of British Columbia) · 2024
Typearticle
Languageen
FieldArts and Humanities
TopicDiscourse Analysis in Language Studies
Canadian institutionsnot available
Fundersnot available
KeywordsAbortionTerminologyReproductive healthBacklashHealth careVariety (cybernetics)Language barrier

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.904
Threshold uncertainty score0.927

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.006
GPT teacher head0.202
Teacher spread0.196 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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