IFC and Canada, Partners in Private Sector Development
Bibliographic record
Abstract
Canada has been an active member of the World Bank Group for over six decades through its thought leadership and financial support. Over the last ten years (2013-2023), IFC’s total financing in projects globally with Canadian clients and project sponsors totaled over 5.1 billion, of which 1.7 billion was IFC’s own account and 3.4 billion was in mobilization with other financing partners. The majority of funding was in oil, gas and mining, followed by electric power. Canada is one of IFC’s largest donors, supporting IFC’s investments and advisory services in all regions and across many sectors with a focus on climate, gender, agribusiness, and improving investment climate. Canada is a significant contributor to IFC's blended finance programs across all sectors and themes, in particular climate finance, with cumulative signed contributions of 702 million. Canada has also been actively supporting IFC Advisory Services with cumulative signed contributions 294 million as of June 30, 2023.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.059 | 0.021 |
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".