Identity, stigma and deviance: A sociological analysis of the Stefonknee Wolscht case
Bibliographic record
Abstract
Introduction:The case of Stefonknee Wolscht, a Canadian transgender woman who publicly identified as an "inner child," was sensationalized and stigmatized by the media. This study analyzed how her identity was socially constructed as deviant, using theoretical frameworks of labeling, stigma, and symbolic control.Methods:A mixed-method approach was applied, combining theoretical-documentary analysis with a structured survey administered to 50 adult participants selected through non-probabilistic convenience sampling. The questionnaire consisted of 13 Likert-scale items grouped into three thematic axes: perceptions of stigma, media influence, and acceptance of diversity.Results:The findings showed that most respondents rejected the idea that non-normative identities represent social deviance. Participants expressed critical views of the media, seen as agents that reinforce stigma and distort diverse identities. There was also a high level of support for legal protection and social respect toward gender diversity and non-traditional identities.Conclusions:The study concluded that deviance is not an inherent individual condition, but rather a social construct shaped by power relations and normative discourses. Wolscht’s case functioned as a catalyst for cultural tensions surrounding identity and social normalcy. Educational, media, and legislative interventions were recommended to promote more inclusive, respectful, and pluralistic communities.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.024 | 0.016 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".