Towards a shared vision for addressing student equity, diversity and inclusion in STEM disciplines
Bibliographic record
Abstract
The Science Student Diversity Initiative (SDI) at the University of British Columbia (UBC) has a mandate to embed strategies and practices of Inclusive Excellence (IE) (Williams et al., 2005) into all areas of student experience. The Science SDI will specifically focus on addressing equity, diversity and inclusion (EDI) in science courses and curriculum at our institution through building capacity to sustain best practices and to build an inclusive campus culture.\nThe literature on EDI in STEM is vast, and emerges from different scholarly communities, each with distinct research traditions and concerns. These sources include educational work in K-12 contexts, educational psychology, and discipline-based education research, all of which contribute to our knowledge of how to understand and best serve the experiences of underrepresented groups in STEM. The IE framework that we will employ has been used extensively in the context of North American institutions but not in the context of Canadian institutions. As part of the Science SDI work, we will explore ways to effectively adapt/adopt the IE framework.\nModelling a recently held faculty SDI workshop at the Faculty of Science at UBC, in this workshop, we will invite participants to create a shared vision for EDI in STEM disciplines in the Canadian context. The workshop will explore three main ideas: identifying current challenges and opportunities, learning from participants’ practices and institutions, and visualizing a campus culture in which all students are equitably supported to succeed. Using a strengths-based approach, participants will engage in a visioning exercise. Overall, the workshop will build knowledge and capacity for educators to begin to address equity, diversity and inclusion in their own institutional contexts.
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 | 0.114 | 0.043 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.037 | 0.062 |
| Scholarly communication | 0.041 | 0.022 |
| Open science | 0.010 | 0.072 |
| Research integrity | 0.017 | 0.039 |
| 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".