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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.030 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.008 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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; both teacher heads agree on what is shown here.
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".