It is just not fair: the Endangered Species Act in the United States and Ontario

The United States and the Canadian province of Ontario have enacted endangered species laws that regulate private land. The rationale for this is that the vast majority of endangered species in the two countries rely on private lands for survival. However, from a landowner perspective the law is deemed unfair. This paper presents analysis from 141 interviews with landowners in three U.S. states and Ontario. In recognition of distributive justice claims, both the U.S. government and the Ontario government have enacted programs aimed at increasing financial incentives for participation and compliance with the law. However, the law is still perceived as unfair. The central argument of this paper is that future amendments and new policies for endangered species should confront two other forms of environmental justice: procedural justice and justice-as-recognition. Landowners in both countries expressed not only concerns about compensation, but also a deep desire to be included in the protection and recovery process, as well as to be recognized by government and society as good stewards of the land. The paper concludes by stating that future policy amendments need to address justice-as-recognition if endangered species conservation on private lands is to be considered fair by landowners.

Exploring social representations of adapting to climate change using topic modeling and Bayesian networks

When something unfamiliar emerges or when something familiar does something unexpected people need to make sense of what is emerging or going on in order to act. Social representations theory suggests how individuals and society make sense of the unfamiliar and hence how the resultant social representations (SRs) cognitively, emotionally, and actively orient people and enable communication. SRs are social constructions that emerge through individual and collective engagement with media and with everyday conversations among people. Recent developments in text analysis techniques, and in particular topic modeling, provide a potentially powerful analytical method to examine the structure and content of SRs using large samples of narrative or text. In this paper I describe the methods and results of applying topic modeling to 660 micronarratives collected from Australian academics / researchers, government employees, and members of the public in 2010-2011. The narrative fragments focused on adaptation to climate change (CC) and hence provide an example of Australian society making sense of an emerging and conflict ridden phenomena. The results of the topic modeling reflect elements of SRs of adaptation to CC that are consistent with findings in the literature as well as being reasonably robust predictors of classes of action in response to CC. Bayesian Network (BN) modeling was used to identify relationships among the topics (SR elements) and in particular to identify relationships among topics, sentiment, and action. Finally the resulting model and topic modeling results are used to highlight differences in the salience of SR elements among social groups. The approach of linking topic modeling and BN modeling offers a new and encouraging approach to analysis for ongoing research on SRs.