Development of an Online Platform to Cultivate Collaboration and Connect Post-Secondary Student Leaders Across Canada
Bibliographic record
Abstract
In 2020, ten visionary undergraduate students across Canada were brought together through the 3M National Student Fellowship and tasked with developing a project that furthers STLHE’s mission of enhancing teaching and learning in higher education. To address a need for increased national collaboration among student groups and to unify student leaders in their efforts to transform higher education, we developed an online bilingual resource hub and forum—CANnect: Cross Campus Collaborations—that enables Canadian post-secondary students to learn about and engage in various areas of advocacy and activism across campuses. We worked with STLHE staff to build the online platform and drew from our own networks of student advocacy groups across Canada to populate the website. To ensure the platform’s sustainability and safety, we hired a bilingual moderator to manage the forum content and update resources. In December 2021, 19 months after we began working on the project, CANnect officially launched. Currently, our biggest challenges involve increasing platform usership and engagement, and ensuring the platform’s long-term sustainability. Nevertheless, we see great potential in this project and remain determined to enhance teaching and learning in higher education by creating an accessible and inclusive space to connect student leaders across Canada.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".