Teaching Trade during COVID: Conducting a WTO Simulation through Remote Delivery
Bibliographic record
Abstract
Abstract In Fall 2020, all universities in Alberta went with remote delivery of classes due to COVID-19 restrictions. This provided not only teaching challenges, but also opportunities. Professors at three Canadian universities teaching similar undergraduate courses in international political economy decided to use the challenges/opportunities of COVID-19 restrictions to experiment with a World Trade Organization (WTO) simulation across three campuses through remote delivery. Simulations are frequently used for teaching in political science, but what was unusual was doing it through remote delivery. This paper assesses the effectiveness of the experiment. It traces the origins/evolution of the idea, learning objectives for the students, preparation by the professors to design the WTO simulation, and the experience of the actual simulation. It also addresses the challenges (technological, timing, assignments, grading, student anxiety, etc.). In addition, it identifies the steps that were taken to reduce and mitigate the challenges. It also acknowledges the mistakes that were made by the professors in designing and implementing the assignment. These observations and reflections are informed by the materials that the professors prepared, their thoughts on the experience, and the feedback from participating students (through official student evaluations as well as a special survey instrument). It provides lessons for future online simulations.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".