Questions and doubts in online trans communities
Bibliographic record
Abstract
This thesis explores narrative identity development in online communities, with a focus on the ways community members handle questions and doubts about gender dysphoria, trans identification, and the decision to transition and/or detransition.Drawing on keyword search data for two key terms, internalized transphobia and imposter qui offre du sens, une explication, un sentiment d'appartenance, un but et une direction, et qui mobilise des symboles, puissants mais méconnus, d'affliction et de transformation.Online trans communities 2 provide a more or less unrestricted space for youth to explore and try out identities in disembodied social interactions, access information and social support, and set intentions and expectations for transition.As Marciano (2014) observed: "[t]he Internet's ability to empower users in various ways -from online support to testing and expressing different selves -makes it almost an ideal medium for transgender individuals."Lemma (2022) profiled an adolescent patient who reported that, online, "she was like she imagined she should have always been" (68).Research suggests that online communities and the relationships that form there play a more significant role in the lives of LGBT youth compared to their non-LGBT peers.Hillier and Harrison (2007) found that same-sex attracted adolescents were often more comfortable sharing and exploring their sexuality in online contexts.The report Out Online (GLSEN et al, 2013), found that LGBT youth are more likely (50%) than non-LGBT peers (19%) to have at least one close online friend.Fourteen percent ofLGBT youth said that they had first disclosed their sexual identity to someone online, two-thirds had used the Internet to connect with other LGBT people within the past year, and LGBT youth were more likely to search for health and medical information online than their non-LGBT peers (81% to 46%).Out Online also found that LGBT youth are three times as likely as non-LGBT peers to report being bullied or harassed online, findings that have been echoed by other researchers (Selkie et al, 2020).In this context, online trans communities may provide a "refuge on the web" from "transphobic" and "exclusionary behavior" (Cipolletta et al, 2017;Selkie et al 2020).Bry et al (2018), as well as McInroy and Craig (2015), link access to trans communities online to decreased minority stress among transgender-identifying and gender-questioning youth by providing access to social support and affirmation.Austin et al (2020) set out to determine the "specific ways in which online engagement helps TGD [transgender and gender-diverse] youth cope, heal, and grow in spite of persistent minority stressors across family, school, community, and cultural contexts."Austin et al reported that they found "robust" evidence supporting the "'life saving' impact of the Internet for transgender and gender-diverse youth," offering access to "an affirming space that, for
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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 | 0.017 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.011 | 0.022 |
| Scholarly communication | 0.011 | 0.018 |
| Open science | 0.002 | 0.021 |
| Research integrity | 0.002 | 0.003 |
| 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".