A Community-based Approach to International Student Well-being
Bibliographic record
Abstract
As international students seek their education at a host academic institution, they become important constituents of the fabric of the host community. While academics and practitioners have highlighted the role of multiple stakeholders in supporting international students throughout their journey, they have also cautioned about the fragmented and siloed approach which has resulted in many students falling between the cracks. Acknowledging those challenges, a grassroots initiative was launched to establish a more intentional, collaborative and proactive approach to international students’ well-being in the City of Brampton, a community that is home to many international students. The Improving the International Student Experience Summit was held in July to work collectively towards building a brighter tomorrow for international students.\nIn this webinar, Sheridan will present a draft of Leading Globally, Acting Locally: The Brampton Community Charter on the Safety, Well-Being and Inclusion of International Students — a roadmap for making Brampton a best-practice leader in supporting international students. Moderated by Amira El Masri with panelists Roopa Desai Trilokekar and Rajan Sandhu.
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 | 0.013 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.021 | 0.013 |
| Scholarly communication | 0.018 | 0.010 |
| Open science | 0.003 | 0.031 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.016 | 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".