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Record W2352530608

Bilateral aid in Canada's foreign policy : the human rights rhetoric-practice gap

2013· dissertation· en· W2352530608 on OpenAlexaboutno aff
Ken Kellett

Bibliographic record

VenueOpen ULeth Scholarship (OPUS) (University of Lethbridge) · 2013
Typedissertation
Languageen
FieldSocial Sciences
TopicInternational Development and Aid
Canadian institutionsnot available
Fundersnot available
KeywordsHuman rightsCommonwealthNormativeForeign policyPolitical scienceState (computer science)PoliticsRhetoricDevelopment aidInstitutionalismInternational relationsPublic administrationLaw and economicsPolitical economyDevelopment economicsLawSociologyEconomics
DOInot available

Abstract

fetched live from OpenAlex

Successive Canadian federal governments have officially indicated their support of human rights in foreign policy, including as they relate to aid-giving. This thesis quantitatively tests this rhetoric with the actual practice of bilateral aid-giving in two time periods – 1998-2000 and 2007-2009. This, however, revealed that Canada has actually tended to give more bilateral aid to countries with poorer human rights records. A deeper quantitative analysis identifies certain multilateral memberships – notably with the Commonwealth, NATO, and OECD – and the geo-political and domestic considerations of Haiti as significant and confirms a recipient state’s human rights performance is not a consideration. These multilateral relationships reflect state self-interests, historical connections, security, and a normative commitment to poverty reduction. It is these factors that those promoting a human rights agenda need to contemplate if recipient state performance is to become relevant in bilateral aid decisions. Thus, it is necessary to turn to international relations theory, in particular liberal institutionalism, to explain Canada’s bilateral aid-giving in these periods.

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)0.006
metaresearch head score (Gemma)0.012
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 score0.220
Threshold uncertainty score0.905

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.005
Science and technology studies0.0190.012
Scholarly communication0.0130.003
Open science0.0010.004
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0070.000

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 head0.037
GPT teacher head0.315
Teacher spread0.278 · 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

Citations0
Published2013
Admission routes1
Has abstractyes

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