Bibliographic record
Abstract
This project has two purposes.The first is to provide readers with a descriptive overview of a simulation of a World Trade Organization (WTO) dispute settlement case (being European Communities-Measures Affecting Wine Imports).The simulation took place in a graduate level international trade course.Secondly, and arguably more importantly, the purpose of this project is to examine the usefulness and effectiveness of simulations as a teaching tool in international studies classes.Simulations are one of three interactive learning techniques that provide unique benefits not realized through traditional teaching methods.This project provides an overview of the WTO dispute settlement mechanism, discusses the actual dispute that was simulated and describes how the classroom simulation unfolded.The final analysis is conducted under the guise of four questions.Was the simulation an accurate reflection of what actually takes place?What significance did the simulation have for students of Canadian trade policy?Did the simulation contain the five major components as recommended by the literature?And, was the simulation effective?The final question uses a behaviour-content matrix, based on Bloom and Krathwohl' s Taxonomies for the Writing of Educational Objectives.It is informed by interviews with the course instructor, student questionnaire responses and the author's own personal assessment as both a student who took the course and as a professional who works in the trade law field.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.000 |
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".