HYBRID POLICY IN NEW JERSEY BEAR MANAGEMENT CONFLICT

ABSTRACT

Bear management in New Jersey is controversial. The Division of Fish and Wildlife’s policies have not reassured factions who oppose their tools, namely hunting, despite being informed by science. This study applies qualitative content analysis to 43 policy documents to understand how actors pursue policy objectives, and how they are shaped. I find that actors pursue their policy objectives by making hybrid arguments that, in addition to science, involve history, values, and institutional power. Thus, they make scientific claims—or claims to what the science might mean—that are incompatible with each other. This study identifies four ways in which this occurs: science gets framed, science gets applied to certain ends, science gets weaponized, and science gets minimized. Comprehending the ways in which these factors supplement and frame science could reduce conflict, improve communication, and build consensus.

Keywords:

• black bears
• hybridity
• New Jersey
• policy conflict
• wildlife management

Disclosure statement

No potential conflict of interest was reported by the author(s).

Supplementary material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/00167428.2022.2119854

Notes

¹ The eleven indicators are: 1) provide publicly-available management information, 2) provide measurable objectives, 3) estimate realized hunting rates, 4) explain how realized hunting rates are estimated, 5) report quantitative information about populations, 6) explain how population parameters are estimated, 7) measure uncertainty in population parameter estimates, 8) respond to public inquiry, 9) explain technique for setting quotas, 10) subject management plans to any review, 11) subject management plans to external review. The ones New Jersey included are: 1, 3, 4, 5, 6, 8, and 9. The authors explain that what constitutes science is often uncertain. This paves the way for social conflict over it, especially if some of the missing indicators include ones which make the process more transparent or verifiable.