The Canadian Consortium of Science Equity Scholars – a multi-institutional approach to improving equity and sense of belonging in the classroom.
Bibliographic record
Abstract
The Canadian Consortium of Science Equity Scholars (CCSES) is a multi-institution group of educators and researchers committed to improving equity in post-secondary science education. We seek to rectify the dearth of EDI data in a Canadian context to ensure that we have the information we need to understand our students’ experience and improve teaching practices to support all students. Our research focuses on the affective dimensions of the classroom and how students perceive themselves and what helps them develop their identity in STEM. To gather information on how students’ sense of belonging and self-efficacy (belief in one’s own capacity to succeed) is impacted by classroom climate and teaching practices, we are surveying students in biology, chemistry, and physics first-year classes across Canada. Data from these surveys will help us identify inequities, but also inclusive teaching practices and the impact they have on different demographic groups. In this session, we will present preliminary results from the initial year of data collected at five Canadian universities, and discuss how this research can inform how we structure programs and courses to reduce systemic and structural barriers to student success. This session will provide participants with ideas on how to better engage their students and improve their sense of belonging and self-efficacy, but also help build connections within the teaching and learning community to collect data to better understand student experiences across science disciplines and institutions to improve teaching practices. This project was approved by the research ethics boards of all the institutions where data were collected.
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 | 0.038 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.029 | 0.008 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.004 | 0.024 |
| Research integrity | 0.002 | 0.005 |
| 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".