Trans intracommunity support & knowledge sharing in the United States & Canada: A scoping literature review
Bibliographic record
Abstract
Intracommunity support and knowledge sharing is used in transgender ("trans") communities as they navigate systems of oppression. Even for individuals with access to accepting families and service providers, connecting with other trans individuals can provide useful insight and resources. Conducted between December 2018 and April 2019, this scoping literature review examines the extent, range and nature of research activity regarding intracommunity knowledge and support sharing within trans communities. Drawing from Weeks' framework regarding sexual communities and the concepts of positive in-group identity and group-level coping, this review specifically addresses the guiding questions of what, how and why knowledge and support are shared within trans communities. Empirical studies conducted between 2004 and 2019 in the United States and Canada that were published in English and had a discernible trans sample or subsample were included. Thirty-one studies met the criteria. Key themes from this literature are (a) sharing information related to advocacy & education, identity/expression and personal stories/experience while (b) utilising an array of both online and/or in-person methods in order to (c) build community/support and navigate identity development and transition. Implications from the review, including impacts on both clients and students in social work and care programs, are discussed.
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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 | 0.010 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.032 | 0.047 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".