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Record W2042376240 · doi:10.1177/0020702015577920

The white paper impulse: Reviewing foreign policy under Trudeau and Clark

2015· article· en· W2042376240 on OpenAlexaffabout
M. Elizabeth Halloran, John Hilliker, Greg Donaghy

Bibliographic record

VenueInternational Journal Canada s Journal of Global Policy Analysis · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Identity and History
Canadian institutionsGlobal Affairs Canada
Fundersnot available
KeywordsHouse of CommonsForeign policyWhite paperWhite (mutation)PopularityPublic administrationGovernment (linguistics)Political sciencePoliticsLaw

Abstract

fetched live from OpenAlex

Three times in the span of 12 years (1968–1980), the foreign policy of the Canadian government was subjected to review by the Department of External Affairs. Although only the first of these efforts resulted in a white paper formally tabled as such in the House of Commons, subsequent reviews tended to follow the design of the first: a comprehensive examination of all aspects of the country’s foreign policy, led and coordinated by senior officials in External Affairs, drawing to varying degrees on expertise from other government departments and the private sector. In all cases, the reviews were intended to produce a document that would guide future policy. They served as useful tools not only for new governments seeking to differentiate their policies from those of their predecessors, but also for those in search of answers to challenges arising in the course of their mandates. This article analyzes the reviews undertaken between 1968 and 1980 and the circumstances that gave rise to them in an effort to account for the popularity of the white paper process among policymakers and to explore the process’s influence on policies subsequently pursued.

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)0.084
metaresearch head score (Gemma)0.197
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: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.744
Threshold uncertainty score0.509

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0840.197
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0110.012
Science and technology studies0.0090.012
Scholarly communication0.0160.005
Open science0.0020.002
Research integrity0.0060.006
Insufficient payload (model declined to judge)0.0020.001

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 head0.016
GPT teacher head0.311
Teacher spread0.295 · 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

Citations5
Published2015
Admission routes2
Has abstractyes

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