Canada: The State of the Federation 2015
Bibliographic record
Abstract
Renewing and expanding national infrastructure is critical to the wellbeing and productivity of Canadians and is one of the foremost challenges confronting our federal, provincial and municipal governments. Not only are the required investments dauntingly large for all three levels of government, but so too is the required level of intergovernmental cooperation if our goals are to be realized. The 2015 State of the Federation volume advances our understanding of these infrastructure challenges and identifies how best to resolve them. The contributors to the volume provide historical or international comparative perspectives and utilize legal, economic, or administrative approaches to examine the nature and magnitude of the so-called infrastructure deficit and the question of how best to finance the necessary investments. The possible roles played by deficits and debt are considered, together with options such as public-private partnerships and asset recycling, and a possible Aboriginal resource tax to finance the on-reserve infrastructure needs of First Nations. Considerable attention is also paid to pricing the use of infrastructure both to achieve efficiency in use and to avoid excess demand and an exaggerated perception of the required level of investment. Other contributors examine the infrastructure-investment-decision processes at the federal and provincial levels and consider the optimal allocation of responsibility for infrastructure investments among the different levels of government, and the related issue of the role of intergovernmental transfers to underwrite this allocation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.074 | 0.016 |
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".