Open for Business: A Panel on Creating International Research Opportunities with Canadian Universities and IS Researchers
Bibliographic record
Abstract
International research collaborations are essential for advancing knowledge and fostering innovation in Information Systems (IS). Given Canada’s strengths in IS research and its robust funding and innovation ecosystem, including support from SSHRC , Mitacs and other granting bodies and technology incubators/accelerators (such as Québec Tech ) there is a growing opportunity for global researchers to engage with Canadian institutions. This panel will explore pathways for international scholars to collaborate with Canadian IS researchers, addressing key challenges such as funding structures, institutional policies, interdisciplinary integration and entrepreneurial ecosystem growth. Featuring leading experts in IS research, Canadian funding bodies and technology incubation, this discussion will provide practical insights on building sustainable research partnerships that generate both academic and practical outcomes. Attendees will gain a deeper understanding of Canada’s research landscape, funding mechanisms, and best practices for fostering impactful international collaborations with tangible results.
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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 | 0.066 | 0.040 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.058 | 0.015 |
| Scholarly communication | 0.027 | 0.009 |
| Open science | 0.007 | 0.023 |
| Research integrity | 0.022 | 0.024 |
| Insufficient payload (model declined to judge) | 0.014 | 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".