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Record W2745459350 · doi:10.1177/0020702017725040

A twenty-first century foreign policy for Canada in the Middle East and North Africa

2017· article· en· W2745459350 on OpenAlexaffabout
Aisha Ahmad

Bibliographic record

VenueInternational Journal Canada s Journal of Global Policy Analysis · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicPolitics and Conflicts in Afghanistan, Pakistan, and Middle East
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMiddle EastState (computer science)IdeologyForeign policyPolitical economyPolitical scienceEthnic groupEconomyDevelopment economicsSociologyPoliticsEconomicsLaw

Abstract

fetched live from OpenAlex

The security crises facing the Middle East and North Africa are among the most complex and volatile in the world today. These protracted conflicts are shaped by a range of catalysts, including state failure, environmental stress, entrenched criminal war economies, ethnic and tribal hostilities, and ideological extremism. Not only are these highly fragmented conflicts, but the multiple actors in each of these theatres also have transnational linkages that trigger contagion effects across state borders. To understand these new twenty-first century security crises, scholars and policy practitioners alike require modern analytical tools. Gone are the days when Canadian foreign policy experts could rely on classic state-based models to explain and predict global violence. To address these contemporary challenges, Canada must adopt a forward-looking approach to the Middle East and North Africa, which does not shy away from the compound and multidimensional challenges that these complex environments present.

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)0.003
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.174
Threshold uncertainty score0.958

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0330.009
Scholarly communication0.0120.003
Open science0.0020.003
Research integrity0.0070.006
Insufficient payload (model declined to judge)0.0060.000

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 head0.045
GPT teacher head0.306
Teacher spread0.261 · 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 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

Citations2
Published2017
Admission routes2
Has abstractyes

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