Bibliographic record
Abstract
Stan StevenSIn the late nineteenth century, the settler countries of Canada, the United States, Australia, and New Zealand were in the vanguard of what became a global movement to conserve nature through the creation of uninhabited national parks and other protected areas.Very often these were on Indigenous lands.In these countries, conservationists and government officials typically saw Indigenous peoples as threats to conservation, a view that rationalized their displacement and legitimized the appropriation of their territories as protected areas, through nationalization and state governance.Initially, these exclusionary protected areas focused on preserving scenic wonders and charismatic species for the public enjoyment and benefit of settler societies, but later they were pre-eminently dedicated to conserving biological diversity.Through much of the twentieth century, most conservation organizations and influential bodies such as the International Union for Conservation of Nature (IUCN) promoted this "fortress conservation" approach to establishing exclusionary, uninhabited, "Yellowstone model" national parks and other protected areas.In recent decades, however, human rights advocates, international treaty monitoring mechanisms, courts, and many conservationists and conservation organizations -including the IUCN -have joined Indigenous peoples in rejecting many of the core assumptions, policies, and practices associated with fortress conservation.They are calling for far-reaching rethinking of conservation, including the reform of protected area establishment, design, governance, and management.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.584 | 0.538 |
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".