Canadian-East African Learning Internships amidst COVID-19: Impacts of Virtual Learning and Exchange on Collaborative Relationships, Trust, and Power
Bibliographic record
Abstract
The formation of trusting relationships is a stated aim of student mobility programs, but resources to support virtual relationship-building in the absence of in-person student travel to international locations have yet to be developed. Towards informing the development of such resources, especially for the context of North-South partnerships, qualitative research was conducted based on the experience of a 2020 summer cohort of remote interns at a Canadian university and an East African partner supervisor. Thematic analysis of intern blogs and semi-structured interviews indicates several considerations and recommendations that, if considered prior to virtual international student learning opportunities, could optimize learning and North-South relationship-building potential of these opportunities. These considerations and recommendations include: recognizing that the formality of online communication can limit feelings of trust and closeness between participants; infrequent communication can impact relationship-building; self-reflexive practice must be intentional to optimize learning in virtual internships; open communication makes a difference to learning and relationships; and, internships would benefit from the development of internship-specific strategic plans. More robust research on remote international internships is warranted to build up understanding of how and why virtual student international internships in general, and virtual internships involving Global North-South partnership in particular, differ from in-person internships.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.036 | 0.007 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 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".