Pathways and Models: Recent Literature on Communication of Research Information

A gap in the flow of information often exists between science and policy. This gap commonly occurs in the science-policy interface. Many social processes and components may exist at the interface in order to bridge information flow. Within the interface, a complex meshwork of models (about knowledge transfer, science-policy advice, influence), pathways, and organizations can create efficient means for meaningful transfer of information from researchers to policy makers. In this post, we present a review of selected recent literature that aims to explain knowledge transfer processes of information for policy and decision-making.

Knowledge transfer is often perceived as a simple process from the point of view of stakeholders; however, little consensus is found in descriptions of the knowledge transfer process from the scientific and academic perspectives. Various models of knowledge transfer have been developed in response to different needs and circumstances. The most common is the problem-solving model. However, others have also been proposed, such as the political model and the interactive model (Olesk, Kaal, & Toom, 2019). An increasing number of actors are involved in these communication models, which has created new challenges for understanding the advice that is presented. Sokolovska et al. (2019) identify three models of scientific policy advice that have evolved over time since the 1960s: linear, interactive, and embedded models. The latter, which has frequently been applied since the 2000s, addresses the shortcoming of the previous models. It incorporates more actors but often requires compromises from all parties to ensure the role of each actor is appropriately fulfilled.

Various organizations and individuals in the embedded model act as knowledge brokers to assist knowledge transfer from researchers to policy makers. Olesk et al. (2019) described a case that highlighted the creation of scientific councilors in Estonia whose initial purpose was to serve as knowledge brokers. However, this intent was not realized as the scientific councilors took on more administrative roles, even though they spoke strongly about the importance of their original plan, namely, to appoint informed policy advisors to ease information communication between researchers and policy makers (Olesk et al., 2019).

The structures and institutions involved in science-policy advice may have common characteristics; however, they often differ from one country to another to reflect societal practices at the various decision-making levels. Sokolovska et al. (2019) discuss these structures, labelling them advisory councils. Such councils can be composed of: academics and stakeholders brought together to address broad issues; expert committees that resolve very specific issues in society; national academies, which are composed of academics to present science-policy interface information; and chief scientific advisors for specific scientific matters relevant to the government (Sokolovska et al., 2019).

Depending on context, science-policy advisors can often play the role of a bridger organization. Wilson and MacDonald (2018) present a case study about resource management issues in the Bay of Fundy region of Nova Scotia, which involved many stakeholders. In this case, bridger organizations functioned in the roles of information mediator, coordinator, and connector (Wilson & MacDonald, 2018). The results showed that non-governmental organizations (NGOs) and government departments and agencies proved most effective in these bridger roles. However, NGOs often face their own challenges in fulfilling their mandates, such as, reliance on external funding (Wilson & MacDonald, 2018). Sarkki et al. (2019) discuss other dynamics of bridger organizations in their role in the science-policy interface, and labelled them as Science-Policy Interface Organizations (SPIORGs).

With various communication models and organizations interacting within science-policy interfaces, specific nodes (conditions) within the governance meshwork can affect the impact that organizations and advisors have on information transfer. Sarkki et al. (2019) list numerous nodes in information transfer, e.g., organizations mandated to deal with particular subjects, funding organizations, laws and regulations, and opposing and conflicting stakeholders. The presence of the nodes can positively or negatively affect the impact organizations have on knowledge transfer. For example, the mandate(s) that govern the work of an organization (e.g., climate change mandate of a climate change-centered organization) may strengthen the impact that organization has on knowledge transfer, possibly by further validating the organization’s initiatives and increasing its influence.

Howlett and Craft (2013) defined additional models of information transfer specifically related to the idea of influence in knowledge transmission. An earlier locational model stated that the physical location of the knowledge provider in relation to the policy maker affected information transfer (Howlett & Craft, 2013). For instance, a minister would be influenced more by his/her senior staff than by a subject expert working in another department. However, similar to the models discussed in Olesk et al. (2019), as time advanced and reliance on evidence-informed decision making increased, the locational model was replaced by new, more content-focused models.

The models of “speaking truth to power of ministers” and “sharing truth with multiple actors of influence” were established, both of which highlight the quality and type of content. These approaches were designed to hold evidence to the same (or higher) level of influence in decision making as in the location concept in information transfer (Howlett & Craft, 2013). Since these models placed content at the forefront, Howlett and Craft discussed the level of influence associated with two types of content, namely, “hot” short-term/reactive content and “cold” long-term/anticipatory content. The degree of influence of hot and cold content varies under different conditions, as is the case when hot advice is used when a decision process faces temporal constraints as opposed to cold advice, which is used in other situations when more research can be conducted (Howlett & Craft, 2013).

Not only is information content a factor in the level of influence in these newer models, but the persuasiveness and trustfulness of the source also plays a large role (Wilkins, Miller, Tilak, & Schuster, 2018). According to the Elaboration Likelihood Model, which explains the cognition of persuasion, the influence of a source is based on several components, i.e., symbols of authority, physical attractiveness, and familiarity (Wilkins et al., 2018). Based on a survey undertaken to determine the degree of influence of different sources, Wilkins et al. (2018) concluded that individuals tend to trust information from educational institutions, scientific institutions, and family and friends most.

The influence of information and knowledge transfer is also greatly affected by the communication channel (or method) used to disseminate information. Wilkins et al. (2018) state that the influence of a communication channel depends heavily on demographics and location, the information being sought, and the urgency of obtaining the information. The aforementioned survey also showed individuals tend to draw information mainly from personal experience and online material (Wilkins et al., 2018).

A complex meshwork of models, organizations, and other factors at the science-policy interface affect how well information is transferred from knowledge generators (researchers) to end-users (policy makers). To overcome difficulties in the transmission of information, many conditions can be reinforced to enhance the work of different knowledge brokers in the knowledge transfer process. For example, Olesk et al. (2019) argued that the application of open science principles and practices to make data and information more openly available and transparent to all (not solely to researchers) could support knowledge transfer by ensuring all individuals—especially policy makers—are more aware of information relevant for decision making. It could also be argued that by providing access to more scientific information policy makers may begin to understand the nuances of this type of information better (Olesk et al., 2019).



Howlett, M., & Craft, J. (2013). Policy advisory systems and evidence-based policy: The location and content of evidentiary policy advice. In S. P. Young (Ed.), Evidence-based policymaking in Canada (pp. 27-44). Don Mills, ON: Oxford University Press.

Olesk, A., Kaal, E., & Toom, K. (2019). The possibilities of open science for knowledge transfer in the science-policy interface. Journal of Science Communication, 18(3), 1–17.

Sarkki, S., Balian, E., Heink, U., Keune, H., Nesshöver, C., Niemelä, J., Tinch, R., Van Den Hove, S., Watt, A., Waylen, K. A., & Young, J. C. (2019). Managing science-policy interfaces for impact: Interactions within the environmental governance meshwork. Environmental Science & Policy, S146290111630332X.

Sokolovska, N., Fecher, B., & Wagner, G. G. (2019). Communication on the science-policy interface: An overview of conceptual models. Publications, 7(4), 64 [15 p.].

Wilkins, E. J., Miller, H. M., Tilak, E., & Schuster, R. M. (2018). Communicating information on nature-related topics: Preferred information channels and trust in sources. PLoS ONE, 13(12), e0209013.

Wilson, L., & MacDonald, B. H. (2018). Characterizing bridger organizations and their roles in a coastal resource management network. Ocean & Coastal Management, 153, 59–69.


Authors: Cali Kehoe and Mikyla Bartlett


This blog post is part of a series of posts authored by students in the graduate course “The Role of Information in Public Policy and Decision Making” offered at Dalhousie University.

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