Strategies for Inclusion of Lesbian, Gay, Bisexual and Transgender Learners in Discipline-based Programs
Bibliographic record
Abstract
Inclusivity is a critical component of Learning Futures and a key pillar of the goal of Education for All established in 1990 by the United Nations Educational, Scientific and Cultural Organisation (2003b). Consequently, many educational institutions have developed programs and policies of inclusion and non-discrimination which apply to members of the school community. However, discipline-based learning programs are grounded in disciplinary communities, whereby learners accumulate work, internship or cooperative experience in the discipline. In these situations, employers and workplace colleagues are not necessarily bound by school inclusivity policies. There is significant evidence that many workplaces are not inclusive. Placing learners in non-inclusive workplaces as a curricular requirement is contrary to principles of inclusivity and threatens the success of learners from marginalised groups. This paper identifies a number of threats to the participation of lesbian, gay, bisexual and transgendered (LGBT) learners in discipline-based learning programs. Strategies for improving inclusion of LGBT learners in school mandated workplace placements are presented.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".