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Record W2621855893 · doi:10.1080/11926422.2017.1331175

Deliverology and Canadian military commitments in Europe circa 2017

2017· article· en· W2621855893 on OpenAlexaffabout
James R. McKay

Bibliographic record

VenueCanadian Foreign Policy Journal · 2017
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicDefense, Military, and Policy Studies
Canadian institutionsRoyal Ottawa Mental Health CentreRoyal Military College of Canada
Fundersnot available
KeywordsNorth Atlantic TreatyPeacekeepingGovernment (linguistics)Political scienceOrder (exchange)IdealismDivergence (linguistics)TreatyPolitical economyPreferencePublic administrationLawSociologyBusinessEconomicsPolitics

Abstract

fetched live from OpenAlex

The Liberal government elected in October 2015 established an operational code based on the concept of “deliverology,” especially on its campaign promises. Such promises included a change in the Canadian contributions against Daesh (actualized), a renewal of Canadian contributions to United Nations-led “peacekeeping” (not yet actualized), and the maintenance of Canadian commitments to North Atlantic Treaty Organization operations (to be increased). This divergence between promises and decisions presents a puzzle. Why would the government fulfill some promises but not others? The increase in the future military commitments comes at a cost, and the government’s efforts to reconcile its costs and commitments in the future will reveal the depth of its idealism. The challenge for future research will be trying to discern between a preference for being a “reliable ally” and the need to be perceived as the same in order to enable the pursuit of liberal internationalist goals.

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)0.002
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.113
Threshold uncertainty score0.820

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.007
Science and technology studies0.0090.003
Scholarly communication0.0100.002
Open science0.0010.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0320.002

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 head0.053
GPT teacher head0.254
Teacher spread0.201 · 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 designNot applicable
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
Published2017
Admission routes2
Has abstractyes

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