Like a squirrel, but bigger: applying human dimensions research to a European bison reintroduction project in the Netherlands
Bibliographic record
Abstract
Managing wildlife and natural resources is no longer an insular issue of managing the natural sciences and processes involved. With an increasingly human dominated landscape, managers must now consider the social element of all management decisions. Incorporating the Human Dimensions of Wildlife Management (HDWM) is more commonplace in management decisions in North America but is still a relatively emerging concept for European decision-makers. The more human-dominated a landscape, the more intertwined social sciences become to natural resource decision-making. Incorporating HDWM to natural resource and wildlife management decisions can increase public participation in the planning and decision-making process. This thesis studies a European bison reintroduction in The Netherlands, the most human-dominated landscape in western Europe, through a human dimension lens. HDWM is an important element for reintroduction projects as this information can help managers gain public trust and ownership towards them. Recreational users of the reintroduction area were surveyed to determine base knowledge levels, perceptions, attitudes, and values towards European bison and ecosystem services provided by their presence on the landscape. Through the information gathered, this study determined that recreational users were generally supportive of bison on the landscape and understood there was some value in the species being present. However, it also shows knowledge gaps towards European bison themselves and the ecosystem services they provide.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".