Authenticating and Legitimizing Transgender and Gender Non-conforming Identities Online: A Discourse Analysis
Bibliographic record
Abstract
The number of Transgender and Gender Non-Conforming (TGNC) individuals who are presenting for counseling is increasing; yet counselors receive little to no exposure to gender-diversity throughout their education and training. TGNC individuals have reported receiving discriminatory experiences within therapy and ineffectual outcomes. Consistent with social-justice practice, knowledge of how clients understand themselves is necessary to enhance the outcomes of counseling. A key resource TGNC individuals are using to engage in identity exploration is online communities. We applied discourse analysis to analyze the talk and text of three such online communities, and explored: “How do self-identifying TGNC individuals construct their identity when they discuss their related experiences online?” We identified that individuals made sense of their identity using three discourses: (a) felt sense, (b) authenticity, and (c) legitimacy. Individuals constructed their identity using linguistic resources to resist systemic oppression and claim their identities as valid and real. We offer suggestions for infusing this insight into trans-affirmative practice.
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 | 0.017 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.013 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.008 |
| 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".