Mobilising knowledge on newcomers: Engaging key stakeholders to establish a research hub for Alberta
Bibliographic record
Abstract
As immigration to Canada increases, so, too, do the complexities associated with serving various groups of newcomers, including immigrants, refugees, temporary foreign workers and international students. A range of stakeholder groups, such as grassroots community organisations, immigrant service provider organisations and academic researchers, have developed knowledge about how to best serve newcomers as they integrate into life in Canada. To date, there have been few opportunities for members of these and other stakeholder groups to work together to ensure that the needs of newcomers are being efficiently met. In this article, we describe a multi-step process of reciprocal knowledge engagement involving diverse stakeholders and led by the Newcomer Research Network at the University of Calgary. This engagement has the ultimate goal of developing a knowledge mobilisation hub focused on building capacity in community-engaged research with newcomers. In order to understand how we will reach this goal, this article outlines the efforts, priorities, challenges and important lessons learned that occurred as part of the multi-step process undertaken to establish a knowledge exchange with newcomer communities at its core.
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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 | 0.022 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.037 | 0.010 |
| Scholarly communication | 0.017 | 0.007 |
| Open science | 0.004 | 0.029 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 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".