A More Durable Relationship—The Case of Canadian Funding of Indian Infrastructure
Bibliographic record
Abstract
India needs to spend 7%–8% of her GDP on infrastructure, while the actual expenditures are much less. As a result, there is a large infrastructure financing gap. Canadian pension funds are helping to bridge this gap and are very active in the Indian infrastructure market. It has been estimated that the cumulative investment of Canadian pension funds into India is over US$75 billion. India courts pension fund investments into infrastructure, like the rest of the world, as they do not suffer from asset-liability mismatch associated with bank financing of infrastructure. Investment in Indian infrastructure also produces handsome returns for these investors. This mutual utility of Canadian pension fund investment into Indian infrastructure makes such investments durable and long term with little chance of change in strategy based on short-term events, like the recent diplomatic spat between the two countries. JEL Classification: O16, O18
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.017 | 0.004 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.024 | 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".