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Record W2555398879 · doi:10.1080/11926422.2016.1250656

50 Years of NPSIA: reflections from directors

2016· article· en· W2555398879 on OpenAlexaffabout
Katarina Koleva, Lance Hadley

Bibliographic record

VenueCanadian Foreign Policy Journal · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Policy and Governance
Canadian institutionsCarleton University
Fundersnot available
KeywordsOutreachPolitical scienceForeign policyGeneral partnershipManagementPublic administrationPoliticsLaw

Abstract

fetched live from OpenAlex

Following World War II, Canada enjoyed an international standing unprecedented in the young nation’s history. Canada’s involvement in the foundation of the International Monetary Fund, and its contribution to the Marshall Plan and the engineering of the United Nations Emergency AQ5 Force, demanded new generations of specialized and skilled foreign policy-minded civil servants. With a donation from Senator Norman Paterson, the Norman Paterson School of International Affairs (NPSIA) was founded to address the issues of the post-war global environment. As a professional school of international affairs located in Canada’s capital, the School has emerged as an integral part of the nation’s international affairs community. To celebrate NPSIA’s 50th anniversary, the Canadian Foreign Policy Journal, in partnership with its policy outreach platform iAffairs, asked past and present Directors of the School about their views on its evolution throughout the years. The following is a brief compilation of transcripts from interviews with Christopher Maule, NPSIA’s Director from 1988 to 1993; Maureen Molot, Director from 1993 to 2002; Fen Hampson, Director from 2002 to 2012; and the present Director, Dane Rowlands, whose tenure began in 2012. The objective of the excerpts is to enrich our understanding of NPSIA’s past and future role as a professional school in the training of Canadian entrepreneurs, advisors, analysts, diplomats and policymakers.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.885
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.034
GPT teacher head0.316
Teacher spread0.282 · 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 designTheoretical or conceptual
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

Citations0
Published2016
Admission routes2
Has abstractyes

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