A Canadian Lens on Facilitating Factors for North American Partnerships
Bibliographic record
Abstract
What does it take to develop and maintain effective international education partnerships between institutions in the Canada, Mexico, and United States? This was a driving question for the qualitative study funded by a Fulbright-Enders-Garcia grant examining the relationship between North American partnerships and campus internationalization. Administrators, professors, and students at four institutions in Quebec, Canada and two institutions in Mexico shared institutional documents and their perspectives on North American partnerships and campus internationalization to help eluminate this relationship. This article features a Canadian lens on what factors contribute to effective partnership development with institutions in Mexico and the United States. The qualitative case study approach yields data on the facilitative institutional documents, administrative structures, and supporting mechanisms that exists within a given institution. Through probing interviews with diverse stakeholders, this approach also yields the human dimension—that is the factors that facilitate individual efforts to craft and maintain partnerships. The piece features primarily the internal workings at work on one side of the partnership equation—in this case within the Canadian institutions. Internationalization champions who are initiating new North American partnerships or seeking to maximize the effectiveness of existing international partnerships are the intended audience. It draws illustrations from the case studies to substantiate the argument that through the combination of visionary leadership, facilitative mechanisms, and competent internationalization leaders who are intentionally collaborating successful partner relationships are established and sustained. It is clearly critical in this global era for institutions to develop international partnerships that advance individual research agendas and student global learning. The issue at hand is how might leaders articulate their vision and organize their institutional structures so that these partnerships contribute as well as they might to advancing student global learning. Drawing elements of good practice from each case, this piece offers a structural model for maximizing partnership engagement.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.041 | 0.033 |
| Scholarly communication | 0.022 | 0.011 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.014 | 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".