Integrating GIS into choice experiments: An evaluation of land use scenarios in Whistler, B.C.
Bibliographic record
Abstract
GIs and choice experiments were integrated to implement a spatial survey and create a GIs-based decision support tool.The discrete choice survey investigated preferences of visitors to Whistler, British Columbia, for alternative land use scenarios at a mountain resort.The hypothetical choice sets, developed in GIs, illustrated different amounts and arrangements of development, protected areas, and recreational opportunities.Visitors preferred resorts with greater amounts of protected areas, especially when protected areas were buffered from development and situated to protect the most ecologically valuable areas.In addition, visitors preferred to limit the amount of development at nodes external to the resort core and tolerated a high percentage of the workforce living in the resort.Finally, visitors preferred only two golf courses, but were indifferent towards the extent of the trail system.A GIs-based decision support tool created using the survey results demonstrates an effective way to communicate the findings.Special thanks are owed to the many individuals who volunteered their time to help make the project a success.
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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 | 0.014 | 0.050 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".