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Record W4247313635 · doi:10.24124/2003/bpgub1247

Sour grapes : simulating a WTO dispute settlement case

2003· dissertation· en· W4247313635 on OpenAlexaffabout
Simone Gobeil

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldSocial Sciences
TopicInnovative Teaching Methodologies in Social Sciences
Canadian institutionsUniversity of Northern British Columbia
Fundersnot available
KeywordsSettlement (finance)Dispute resolutionReflection (computer programming)Political scienceComputer scienceEngineeringPublic relationsLaw

Abstract

fetched live from OpenAlex

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. means of instruction." 2 This project will attempt to conduct such an assessment, using a WTO dispute settlement simulation as an example.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.596
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0030.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.120
GPT teacher head0.470
Teacher spread0.350 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2003
Admission routes2
Has abstractyes

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