Making a Knowledge Translation Platform Through the Leadership of a Student Organization to Enhance Immigrant Community Engagement with the University
Bibliographic record
Abstract
An effective knowledge mobilization platform is important to facilitate optimal usage of the research evidence in society. While public lectures organized by the universities play an important role in engaging the community with the academic research, a more accessible platform is still required to engage the immigrant community as they face language barriers, unfamiliar environments and general reluctance. To address this, the Bangladeshi Scholars’ Association (BSA) has initiated a program whereby graduate students will translate their research work once a year to immigrant communities in Calgary. In our pilot program, we are collaborating with the Bangladesh-Canada Association of Calgary (BCAOC) and the Immigrant and Refugee Health Interest Group in the Department of Family Medicine at the University of Calgary to ensure participation of both general Bangladeshi immigrants and their community-based professional associations. We aim to send approximately 20 students from the University of Calgary to present their research projects in the format of three-minute elevator pitches to the Bangladeshi immigrant community. These presentations will be tailored for a lay audience and will take place at the Bangladesh Center in Calgary to ensure easy accessibility. Because we aim to continuously improve this knowledge translation platform, we will conduct a post-event survey to gather feedback and suggestions from the audience. Overall, we anticipate that this platform will generate enhanced enthusiasm among the Bangladeshi immigrant community about current research activities at the University of Calgary. The success of this event will also form a strong rationale to scale-up this platform for engaging other immigrant communities in future.
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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 | 0.025 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.002 | 0.020 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.025 | 0.011 |
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".