EXPLORING THE CYC CIS-TEM: A LITERATURE REVIEW OF QUEER AND TRANS TOPICS IN CHILD AND YOUTH CARE
Bibliographic record
Abstract
Although Child and Youth Care (CYC) sees itself as a field that embraces diversity and complexity, there is a notable lack of discussion of sexual and gender diversity: queer and trans topics are rarely taken up across CYC research, practice, and pedagogy. Through a systematic literature review of articles published between 2010 and early 2020 in six journals with a focus on CYC practice, research, and theory, this article assesses how queer, trans, Two-Spirit, and nonbinary identities and topics are being discussed in the current CYC literature and reveals a conspicuous absence of publication on these topics. In a 10-year period, across six CYC publications comprising over 4000 published articles, only 36 articles focused on queer and LGBT issues (by covering both sexual and gender diversity) and, of those, only eight articles specifically focused on gender diversity or trans topics. No articles were found within any of the reviewed publications that specifically focused on Two-Spirit identities or topics and only one article mentioned nonbinary identities. Through exploring how and where queer and trans, Two-Spirit, and nonbinary identities and topics are being discussed, this review asks how we as a CYC field might begin to make space for these topics within our field and practice, in order to work towards social change that shifts our field and challenges the cis-heteronormative CYC system.
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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 | 0.013 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.021 | 0.028 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".