Bibliographic record
Abstract
SUMMARY The Federal Provincial Parks Council (now known as the Canadian Parks Council) called for the development of a common framework to measure the economic value of protected areas. The Provincial Economic Impact Model (PEIM) is a standardized tool to estimate the economic impacts of parks in Canada, as part of the common framework. Visitor-spending data and park-budget data are input into the PEIM to generate estimated impacts, measured by labour income, gross domestic product and employment. Five distinct visitor types in Algonquin Provincial Park were examined (i.e., day visitors, car campers, interior visitors, lodge visitors, cottage leaseholders). Visitor information was collected through detailed visitor surveys in 1999 and 2000, and visitor profiles were developed. Average amount spent per person-night for each visitor type was multiplied by yearly park visitor numbers to estimate total annual spending for each visitor type. Using the PEIM, annual spending for these five visitor types was estimated at approximately $20 million. This paper contains a modest selection of study results. This is the first time precise calculations have been done for Algonquin Provincial Park. 1.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".