What makes wildlife wild? How identity may shape the public trust versus wildlife privatization debate
Bibliographic record
Abstract
ABSTRACT Wildlife conservation policy discussions in the United States and Canada often revolve around historical accounts of the success of wildlife management grounded in the public trust doctrine. We suggest that the usefulness of these discussions is partially limited by failure to consider the importance of wildlife “identity” rooted in freedom (i.e., how humans socially construct the “wildness” dimension of wild animals). To demonstrate the interrelations between identity and freedom, we explain that the class of subjects people care most about—partners, children, and people in general—typically should not be privately owned (i.e., chattel) because freedom (as opposed to slavery) is generally accepted as central to human identity, and its abrogation therefore degrades human identity. The degree to which this ethical argument applies to privatization of wildlife depends upon the relationship between freedom and the identity of wildlife as perceived by society. Thus, we suggest policy decisions regarding privatization of wildlife will be more accurately deliberated if society and wildlife professionals more completely considered the degree to which freedom is essential to a wild species’ identity and the degree to which that identity is inviolable. © 2016 The Wildlife Society.
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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 | 0.019 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.050 |
| Scholarly communication | 0.022 | 0.013 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.011 | 0.008 |
| 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".