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Record W4378388302 · doi:10.1080/11926422.2023.2209668

Canadian developmental assistance in Southeast Asia

2023· article· en· W4378388302 on OpenAlexaffabout
David Webster

Bibliographic record

VenueCanadian Foreign Policy Journal · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Policy and Governance
Canadian institutionsThe King's UniversityWestern University
Fundersnot available
KeywordsPolitical scienceSoutheast asiaGovernment (linguistics)Civil societyEconomic growthDevelopment aidConsistency (knowledge bases)Work (physics)Development economicsPublic administrationPoliticsSociologyEconomics

Abstract

fetched live from OpenAlex

Canada has provided development cooperation funding to Southeast Asia since the 1950s, but lacked consistency over time. Aid priorities echoed wider Canadian foreign policy goals and trends in global development thought. Early stress on infrastructure and “basic needs” gave way to “civil society strengthening,” then to corporate partnerships. An early focus on aid to Malaysia shifted to Indonesia, then to Vietnam and the Philippines, and finally to democratizing Myanmar – a trend reversed since military rule. Canada was also briefly a leading donor to Timor-Leste. Government has relied heavily on non-governmental organizations to deliver aid to the region. As NGO freedom shifted into firmer government control, aid effectiveness and Canada’s reputation in the region have been harmed. A new shift within Canada’s “feminist international assistance policy” may herald a return to centering non-governmental voices. This article offers a historical overview of Canadian development assistance work in Southeast Asia. It argues that despite some success stories, changing priorities in Ottawa have hampered overall effectiveness. Canadian governments have too often undermined their own claims to seek long-term economic development through aid. Listening to civil society in Southeast Asia and stronger awareness of Southeast Asian priorities would create more effective aid outcomes.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.954
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.023
GPT teacher head0.279
Teacher spread0.257 · 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 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
Published2023
Admission routes2
Has abstractyes

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