Collaborative Online International Learning (COIL): Fighting Hunger During a Global Pandemic
Bibliographic record
Abstract
Today, an increasing number of higher education institutions are recognizing the importance of preparing students to communicate, live, and work effectively with others from different cultural backgrounds (Appiah-Kubi, 2020; Eliyahy-Levi, 2020; Iuspa, 2019). Providing opportunities for students to travel abroad has been an integral part of the higher education experience for many years. However, with the global pandemic resulting in limited travel, universities are developing new and innovative ways to provide international experiences for students. The purpose of this cross disciplinary project was to explore an international virtual student collaboration between two universities, one in the United States and one in Southeast Asia. Twenty students were matched across universities to examine one of the U.N. Sustainable Development Goals: #2 Zero Hunger (https://sdgs.un.org/goals). Fighting hunger is not a new problem in our world, but the focus has been greater in the midst of a world-wide pandemic. Many people have lost jobs or had their income severely impacted. Students from both universities worked collaboratively to explore the issues of fighting hunger during a pandemic and focused on finding solutions that can last long after the end of this current pandemic.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".