Found Money: Matching Canadians' Saving with Their Infrastructure Needs
Bibliographic record
Abstract
Crumbling bridges and leaking pipes need fixing; new roads and power lines need building as cities cope with growth — Canada needs infrastructure investment. Last fall’s Advantage Canada document from federal finance minister Jim Flaherty made infrastructure a priority. Yet governments face huge bills for health, education and income support. Infrastructure, once a big-ticket item, earns fewer votes nowadays. Meanwhile, Canada is awash in saving. Canadians are aging and seeking to secure their retirements. Cash is flowing into corporate pension plans. Millions of individuals contribute to RRSPs. Huge assets are building in the Canada and Quebec Pension Plans and government-worker funds. This burgeoning saving is a major driver behind Canada’s net flow of funds abroad — more than $30 billion annually since 2004. Impressive as it is alongside Canada’s needs, this dollar amount is only one facet of the opportunity that exists to better match Canadians’ infrastructure demands with their saving. The Canada Pension Plan Investment Board made headlines in the UK in late 2006 when it partnered in a £2.2 billion takeover bid for one of the country’s largest water companies. Private-sector and governmentworker plans are scouting the world for investments in power and gas transmission, water, roads, bridges and tunnels, airports and ports. The reason: these assets are long-lived, with returns that are relatively stable and well correlated with real income growth and inflation — just what a population saving for retirement needs. More Canadian savers, large and small, would do well to get a piece of this action. So what’s wrong? Why do we not fund more domestic infrastructure with domestic saving? One major obstacle is political. Private participation in infrastructure is rare partly because Canadian governments tend to own these things themselves. Even though Canadian organizations such as the Ontario Teachers Pension Plan, Borealis, the Caisse, BC Investment Management Corporation and Alberta
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.002 |
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".