Introducing "The Seeker": Bridging Service and Information Gaps for Professional Immigrants
Bibliographic record
Abstract
As Canada witnesses a growing influx of economic migrants, the need to enhance support for skilled immigrants in their chosen professions becomes increasingly paramount. In response to this imperative, we present "The Seeker," a comprehensive digital platform strategically designed to bridge service and information gaps for professional immigrants within Canada. Our method involves a twophase knowledge-building strategy: an integrative literature review and an all-encompassing environmental scan, followed by a robust mixed methods study. The culmination of these efforts is "The Seeker," an easily accessible online platform that offers a range of resources, including career guidance, insights into workplace dynamics, networking strategies, and more. Content is tailored to Albertaspecific needs and resources and caters to distinct professions and diverse cultural backgrounds. Currently in the piloting stage, our process involves conducting interviews with our target audience to evaluate the platform’s content, alongside a survey to assess its outcomes comprehensively. Initial analysis shows that the platform offers relevant information, enhancing service access to newcomers. Moreover, it streamlines information-seeking and provides timely guidance in their settlement and integration stages. This initiative addresses research gaps, promoting awareness of professional immigrant-tailored services and optimizing integration through increased settlement service utilization. "The Seeker" stands to enrich the experiences of those who seek to establish themselves in Canada’s professional landscape. This project is a collaboration between TIES, The University of British Columbia and the University of Michigan, funded by the IRCC.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.021 | 0.007 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".