Canada's national parks : what are they worth to Canadians and why?
Bibliographic record
Abstract
A double-bounded dichotomous choice contingent valuation question with an open-ended follow-up was used to estimate the value of the net benefits that Canadians receive from Canada's national park system, beyond their current expenditures for the parks. The valuation questions were a small component of the 2005 Parks Canada National Public Opinion Survey. The Survey was administered by telephone by the research firm Environics© and was completed by 6,086 respondents. The valuation questions were administered to a randomly assigned sub sample of 1,308 respondents. Two regression models were used to examine how survey respondents who agreed to pay an unspecified amount compared to those who agreed to pay a specified bid amount. A payment vehicle of an annual or one-time contribution to a non-profit national parks fund yielded a net benefit value of $53.32 per household for the double-bounded question and $69.65 for the open-ended question. About 61% of survey respondents were willing to make a contribution to the fund of which about half were willing to contribute their amount on an annual basis. The final regression models indicated that respondents who agreed to pay an unspecified dollar amount to the national parks fund differed considerably from those who agreed to pay a specified amount. The most important predictor variables for the unspecified dollar model were attitudinal variables such as: the level of respondent support for funding national parks with government funds, exposure to information about the national parks, community size, contributions to nature organizations, the perceived individual responsibility for protecting nature, and the impression of national parks as common or unique places. Conversely, the most important predictor variables for the specified dollar amount model included: the bid amount, the level of agreement statements regarding bequest and option value, region of residence, household language, age, gender, the ability to recall the name of the last national park visited, the number of years since the last national park visit, the level of exposure to information about Parks Canada, the impression of national parks as common or unique, level of education, and volunteered time with a nature protection organization.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".