Non-Compliance with On-Site Data Collection in Outdoor Recreation Monitoring
Bibliographic record
Abstract
ABSTRACT A wide range of methods exists for on-site visitor monitoring in parks and recreation areas. Self-registration methods have proven to be popular because of their low cost and relative ease of administration, but little is known about the extent to which the data collected from self-registration boxes are representative of the population of visitors, and the degree that bias exists as a result of non-compliance. This article examines these concerns based on research at Fulufjället National Park in Sweden. On-site registration card and follow-up mail survey data from a sample of visitors who did not voluntarily register were compared with the same kind of data for visitors who did register voluntarily. In total, 10 registration card items and 284 mail survey items were tested for variations between compliant and non-compliant visitors. Of these, one third of the card items, 12% and 3% of the survey items for Swedish and German visitors, respectively, yielded statistically significant differences. Implications for management and suggestions for further research are discussed.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".