Changing Conceptions of Protected Areas and Conservation: Linking Conservation, Ecological Integrity and Tourism Management
Bibliographic record
Abstract
From their first creation, national parks and equivalent reserves were socially constructed in the New World as static, primordial, untouched representations of a pre-European contact environment characterised by the ‘balance of nature’ resting in a steady (climax) state. While these images still linger in the minds of the public, the recent utilisation of landscape ecology, conservation biology and social constructivism to study and re-conceptualise protected areas has demonstrated that parks are not the protected islands of virgin wilderness they were constructed to represent; rather than protecting these areas from disturbance, we now recognise that disturbance is a major component in ecological integrity. We suggest that the resultant shift from species- to process-based conservation (i.e. ecological integrity), from attempting to cocoon parks from outside influences to re-engaging parks with landscape-level processes, has critical ramifications for protected area and sustainable tourism management. Land managers need to adapt to a new paradigm that reflects and supports this philosophical change in conservation principles; this shift is also reflected in science itself, manifested by a move from normal to ‘post-normal’ science which embraces these new principles. This approach should link visitor expectations with dynamic, non-linear, self-organising natural processes in order to meet conservation objectives.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.004 | 0.109 |
| Scholarly communication | 0.018 | 0.024 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.005 | 0.010 |
| 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".