Six decades of environmental resource valuation in Canada: A synthesis of the literature
Bibliographic record
Abstract
Abstract This paper synthesizes Canada's environmental valuation literature over the last six decades. Focusing on primary valuation benefit estimates, we link multiple research outputs from the same data collection effort to obtain an accurate measure of unique studies. We identify a total of 269 unique valuation studies conducted in Canada between 1964 and 2019. The number of valuation studies conducted per year has not increased since 1975 and the median data collection year is 1996. Stated preference (SP) methods are the most popular valuation approaches being used in more than 50% of studies and this share has increased to over 80% within the last decade. We discuss numerous gaps in our knowledge for certain environmental resources and regions, in particular Canada's three Northern territories. The paper provides information on the state of environmental valuation research in Canada and identifies future research needs.
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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 | 0.016 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.030 | 0.081 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".