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Record W2092876679 · doi:10.1017/s1049096514001206

Teaching Canadian Politics and Foreign Policy Onsite: Benefits and Challenges

2014· article· en· W2092876679 on OpenAlexaboutno aff
James M. McCormick

Bibliographic record

VenuePS Political Science & Politics · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Policy and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsPoliticsLiberal arts educationPolitical scienceExperiential learningThe artsForeign policyState (computer science)Public administrationPublic relationsEngineering ethicsHigher educationEngineeringLawComputer science

Abstract

fetched live from OpenAlex

ABSTRACT This article discusses the benefits and challenges of offering an onsite seminar on Canadian politics and foreign policy and assesses how this format contributes to achieving the goals of the 2011 APSA report, Teaching Political Science in the 21st Century. First, the author describes the development and requirements of the College of Liberal Arts and Sciences Global Seminar series at Iowa State University, the structure of the seminar, and its operation in Ottawa. Second, several of the pedagogical and experiential benefits, as well as the challenges, for making the seminar successful are identified and discussed. Third, by weighing these benefits and challenges, the author concludes that such a seminar has the potential to serve as an effective model for increasing an understanding of Canadian politics among American students, as well as to meet several important recommendations for improving the teaching of political science today.

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 imitation

Not 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.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.188
Threshold uncertainty score0.379

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.013
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0190.004
Scholarly communication0.0150.003
Open science0.0030.008
Research integrity0.0050.005
Insufficient payload (model declined to judge)0.0390.002

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 head0.037
GPT teacher head0.308
Teacher spread0.271 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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

Citations1
Published2014
Admission routes1
Has abstractyes

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