Close encounters: the Thompson Wolf Park
Bibliographic record
Abstract
Interacting with other animal species is critical to human happiness, self-discovery, and an understanding of the broader natural world. Despite these benefits, wild animals have been pushed to the periphery of human existence. The resultant lack of animal contact has adverse affects on humankind’s relationship with the natural world. Close Encounters: The Thompson Wolf Park is a design practicum that sets out to change this. This work includes a detailed review of the landscapes that humans have created to facilitate encounters with animals. The evolution of such landscapes has ultimately resulted in the development of the modern zoo. A critique of zoos is undertaken, with particular emphasis on conservation, ecotourism, exhibit design, and educational and recreational aspects. This work then explores ways to change the form and function of zoos to alter people’s perceptions of nature. A set of design goals is developed for an alternative type of zoo and applied to a real-world wolf park project in Thompson, Manitoba. A study of grey wolves is undertaken to further inform the design. The Thompson Wolf Park is a zoological institution that is intended to excel where its reactive predecessors have faltered, namely in instigating changes in the visitor’s ecological behaviour. By seeing grey wolves in their natural environment visitors will have a restorative, educational, and more holistic nature experience than at the traditional zoo, and be inspired to protect the natural world.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".