Four Canadian Faculties of Music: A Preliminary Investigation into Equity, Diversity, Inclusion, and De-colonization in Post Secondary Education
Bibliographic record
Abstract
This study, conducted through the lens of two Asian and African Canadian students and their lived experiences, examines four institutions, the University of British Columbia, the University of Toronto, McGill University, Western University, and the ways in which each addresses Equity, Diversity, Inclusion and Decolonization (EDI-D). Accessing public documents such as syllabi, course descriptions, event calendars, published articles, strategic plans, released statements from students and faculty, and their online social media platforms, data was analyzed using qualitative document analysis (Altheide, Coyle, DeVriese, & Schneider, 2008). Overall themes were uncovered and examined (Cousins & Bourgeois, 2014; Khan & VanWynsberghe, 2008) including curricular change, methods of transparency, representation, and cultures of institutions. With the use of suggestions and guiding questions the researchers interrogate their findings and provide a way forward for anyone interested in EDI-D work. The following research question guided the research: 1) In what ways are institutions indigenizing their spaces and implementing anti-racist policies? Website: https://westernusri2021.wixsite.com/usrioutput Reflection video :https://www.youtube.com/watch?v=WdzsMO8_Mls
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.047 | 0.009 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".