Employing informal learning theory and network analysis to improve the way we communicate scientific information to fisheries stakeholders

As the understanding of fisheries systems has evolved, so too has the way their management is approached. Traditional, top-down, highly centralised fisheries management is being phased out in favour of models that require the engagement of a wider range of stakeholders. Correspondingly, the ability to communicate effectively and efficiently to stakeholders with a diversity of scientific and experiential backgrounds is becoming a greater priority. This thesis applies informal learning theory to the communication of scientific information to fisheries stakeholders. In the literature review chapters, I identify the failings of old communication models, and discuss how existing theoretical constructs could inform explorations into improving the effectiveness and efficiency of communicating scientific information to stakeholders with diverse scientific backgrounds. I draw upon psychology and formal and informal education models used to understand the uptake of scientific information in sub-sectors of the public, and identify gaps within the existing literature that preclude the immediate application of existing models to the context of scientific fisheries information and fisheries stakeholders. The first data chapter in this thesis identifies variables influencing the informal learning of scientific fisheries information by fisheries managers, researchers and commercial and recreational fishers. Using semi-structured interviews and content analysis, I confirm that the cognitive, conative and affective dimensions used to describe informal learning in the literature apply to fisheries stakeholders. I also identify two constraints: investments of time, and investments of money. In the second data chapter, I demonstrate the mapping of the relationships between these variables using a Fuzzy Cognitive Mapping (FCM) approach to mental models. I show that by combining the FCM approach with network analysis techniques, it is possible to illustrate the relative importance of each variable, whether it is acting as a driver or a constraint, and how closely the variables relate to one another. I also demonstrate that individuals' initial levels of interest in a topic significantly influence their willingness to informally learn more about that topic. Specifically, when fishers are less interested in a topic, their mental models and the relationships between the variables are simpler than when their initial interest is piqued. My third and final data chapter uses network analysis to identify pathways through which the communication of scientific information to fisheries stakeholders could be made more efficient. I examine whether information sources' formats or the authorities they represent affect the likelihood of stakeholders relying upon them, and employ network analysis metrics to identify strategic targets that could benefit communication programs aimed at specific stakeholder groups. The results indicate that the ideal outlet for communicating science to fisheries stakeholders is likely to vary depending on the content and context of the information to the individual, to the degree that in some cases, a stakeholder's primary role has no significant effect