Bibliographic record
Abstract
Canada’s natural capital — its resources, ecosystems and wildlife — are indispensable to the productivity of industry. Despite this, both the public and private sectors have failed to adequately factor in the consequences of production and consumption on the natural environment. There is a growing need for full-cost pricing, a system that adjusts market prices to reflect not only the direct costs of good and services, but also their impact on this country’s natural capital. As this paper argues, the onus is on the federal government to create the conditions for full-cost pricing to succeed. Ottawa needs to eliminate energy subsidies (to producers and consumers), implement full-cost pricing on air contaminants and greenhouse gases and encourage projects at the provincial and municipal levels that adopt that methodology. The benefits include productivity gains; potentially billions in savings for consumers, businesses and governments; a strong environment supporting sustainable industries; and simplified tax systems. In surveying past and existing federal initiatives and missed opportunities in previous budgets, this paper assesses costs and consequences, arguing that a healthy environment is synonymous with a healthy economy, and providing hard data to back up that conviction. With Budget 2012 just around the corner, the time is ripe for the Harper government to introduce full-cost pricing, and guarantee Canada a brighter future.
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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 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".