Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.014 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".