Bibliographic record
Abstract
Park tourism is a large and important activity on which a substantial body of research work has been published. This paper reviews that work in the light of the issues now faced by parks and park tourism, and argues that there are significant research gaps that urgently need additional work. The paper outlines 10 such areas, including: visitor use monitoring; park tourism economic impact monitoring; park finance; professional competencies for tourism management; building public support; visitor satisfaction; licenses, permits, leases, and concessions for tourism; pricing policies; management capacity; and park tourism governance. The paper suggests that work in these areas is so important that the long term political and social relevance, effective management and sustainable future of many parks and protected areas depend on the results. It points to the park creation phase being over after about 150 years of growth, and the need to move more effectively into the long-term management phase. A number of key questions arise. The numerous parks must fulfill their conservation mandate and they must be financially secure. They almost certainly must forge links to tourism, yet not be dominated by tourism's demands, creating, therefore, a new and sustainable research-based relationship.
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 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 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.015 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.021 | 0.002 |
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".