How Effective is Canadian Bilateral Aid in Improving Access to Quality Education in Tanzania?
Bibliographic record
Abstract
Tanzania is among the poorest countries of the world with approximately one third of its population living below the poverty line of less than $1 a day. The country has been heavily reliant on foreign aid assistance, with close to 40 percent of its national budget and up to 70 percent of its education development budget dependent on donor assistance. Moreover, according to OECD calculations, Tanzania is the third largest recipient country of development aid behind Iraq and Afghanistan, having received $2.811 billion in donations. In this project, I reviewed Canadian bilateral assistance to Tanzania during the period of basic education reform in Tanzania, from 2002 to 2011. I reviewed Canada's policy documents on its support for basic education, a breakdown of Canadian aid funds disbursed to support basic education in Tanzania as reported in the Creditor Reporting System of the OECD, as well as reports of Tanzania's PEDP implementation. I argue that Canada's support for basic education in Tanzania may be ineffective because there is no Canadian policy document to guide the country's engagement in ensuring improvement of learning outcomes (quality education); Canada allocates smaller amounts in improving the education policy and administration management in Tanzania than other education components; Canada has shifting funding priorities and overemphasizes funding its national NGOs and CSOs. Also, the efficacy of Canadian aid is constrained by Tanzania's overreliance on donors, institutional gaps facing the country's education ministry, inflation, and lack of legal responsibility and accountability.
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 | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
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.
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".