Use of Single Large or Several Small Policies as Strategies to Manage People–Park Interactions
Bibliographic record
Abstract
Biodiversity conservation has been criticized for undermining or ignoring social well-being. Currently efforts to mutually promote social justice, rural development, and biodiversity conservation, which have been contentious and yielded mixed results, continue to spread despite a general dearth of effective management strategies. We contend that social and economic concerns should be integral to conservation planning and propose that the scale of these phenomena is also critical. To evaluate the merit of this proposal, we adopted and expanded a conservation management strategy framework developed by Joel Heinen and examined how population density, economic disparity, and ethnic heterogeneity vary spatially surrounding 2 contrasting protected areas in East Africa: Kibale National Park in Uganda and Tarangire National Park in Tanzania. Analyses of demographic, wealth, and ethnicity data from regional censuses and household surveys conducted in 2009 and 2010 indicated that choice of scale (landscape or community) changed the management strategies recommended by the model. Therefore, "several small" people-park management strategies varying around a given protected area may be more appropriate than a "single large" people-park strategy applied across an entire protected area. Correspondingly, scale adjusted Heinen recommendations offered new strategies for effective conservation management within these human landscapes not incorporated in current in situ management plans.
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 | 0.011 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".