Exploring Identity Through Two-Eyed Seeing: Insights into 2SLGBTQ+ Student Experiences at Carleton University
Bibliographic record
Abstract
This research examines the experiences of 2SLGBTQ+ students at Carleton University, aiming to gauge their perceptions of institutional support for their diverse needs. The study is grounded in equity, diversity, and inclusion (EDI) qualitative research, focusing on the unique challenges faced by 2SLGBTQ+ students. It employs a methodology informed by Indigenous concepts of two-eyed seeing and positionality, alongside the eight principles of community-based participatory research. Through qualitative methods such as interactive interviews and focus groups, participants are treated as co-creators of knowledge, ensuring their voices remain central. Snowball and purposive sampling techniques are used to ensure diverse representation. Preliminary findings reveal significant challenges in accessing support services and navigating campus life for 2SLGBTQ+ students, compounded by intersecting social identities. Recommendations for policy improvements are anticipated outcomes. The analysis identifies intersectionality as a key determinant of university experiences, along with challenges in resource access and the prevalence of the gender binary. While the university is perceived as 2SLGBTQ+-friendly, this perception largely arises from community efforts rather than institutional support. The study emphasizes the need for greater inclusivity and support in university environments. In conclusion, it highlights the importance of understanding and addressing the diverse needs of 2SLGBTQ+ students and stresses the role of community initiatives and institutional policy in fostering inclusive campus environments.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.021 | 0.013 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".