Evaluation and monitoring of transdisciplinary collaborations (original) (raw)
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Environmental Science & Policy, 2020
Participation of relevant stakeholders, knowledge integration, responsive and emergent design and effective boundary management are four key features of transdisciplinary research (TDR). These features pose significant challenges to both undertaking TDR and evaluating its societal impact. We argue that TDR's context specificity and complexity warrant an evaluation approach that supports the coordinating team in developing these key features. In light of this, this article aims to reconcile two distinct foci of TDR evaluation, namely supporting transdisciplinary capacity building and impact evaluation. We share the results from a combined approach in which the authors acted both as facilitators and evaluators of a TDR project, to conduct an embedded, formative evaluation. Our findings show that the approach allowed for better access to the participants and sensitivity to their perspectives on impact, and for enhanced understanding of complex internal and external project dynamics and how these shaped the project. This resulted in a meaningful assessment of TDR's societal impacts and enabled attributing these to specific process elements. Moreover, the approach supported the coordinating TDR team's capacities for developing key TDR features. Four TDR capacities were identified: building TDR ownership, openness and transparency for integrating divergent TDR needs, purposeful responsiveness to emergent TDR needs and navigating institutional realities and TDR ambitions. The approach presented may serve as stepping stone for the TDR community to further the conversation on (the impact of) inclusive, reflexive and responsive research.
Pathway to Impact: Supporting and Evaluating Enabling Environments for Research for Development
Evaluating Climate Change Action for Sustainable Development, 2017
The chapter presents a research for development program's shift from a Logframe Approach to an outcome and results-based management oriented Monitoring, Evaluation and Learning (MEL) system. The CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS) is designing an impact pathway-based MEL system that combines classic indicators of process in research with innovative indicators of change. We have developed a methodology for evaluating with our stakeholders factors that enable or inhibit progress towards behavioral outcomes in our sites and regions. Our impact pathways represent our best understanding of how engagement can bridge the gap between research outputs and outcomes in development. Our strategies for enabling change include a strong emphasis on partnerships, social learning, gender mainstreaming, capacity building, innovative communication and MEL that focuses on progress towards outcomes. It presents the approach to theory of change, impact pathways and results-based management monitoring, evaluation and learning system. Our results highlight the importance of engaging users of our research in the development of Impact Pathways and continuously throughout the life of the program. Partnerships with diverse actors such as the private sector and policy makers is key to achieving change, like the attention to factors such as social learning, capacity building,
Mapping Alternative Impact: Alternative approaches to impact from co-produced research
2015
• In order to encourage and support co-produced research, RCUK, HEFCE and Universities should expand the concept of "research impact" used. • The "donor-recipient" model of impact, where a single knowledge producer (University/academic) impacts on economy or society in a linear fashion, is not relevant to co-produced research. • Instead, impact is a collaborative, transdisciplinary praxis, that involves collaborators from different backgrounds coming together to undertake research with a common purpose. • This approach of participatory co-production can create additional value-added in terms of both knowledge and impact, as many research projects have successfully demonstrated. • Institutional infrastructure, especially around research funding, research support and impact audit procedures, must shift in order to address the differences in time, openness and relationships required for this approach to reach its full potential.
2016
Scientific Context Without ignoring the existence of a large array of scientific perspectives about the measurement of science productions and science dynamics, we situate our work in the branch of analysis and visualization of social networks. This fiel d - as well as indicators definition - has been an important step forward for the evaluation and policy of science (Callon et al., 1986; Law et al., 1988). Within this tradition of analyzing free - associations in relation to Actor - Network - Theory, t he underst anding of scientific collaboration s had supposed consequent methodological and ethical requirement . This is still very much at stake today in a momentum when the data about scientific activities are continually growing , while the heterogeneity of scientifi c activities is still important despite many attend to rationalize, measure and evaluate its quality and performativity. Besides the necessity of evaluating the performance of normal science and technological creativi...
Transdisciplinary partnerships for sustainability: an evaluation guide
Sustainability Science, 2022
Transdisciplinary research, in which academics and actors from outside the academy co-produce knowledge, is an important approach to address urgent sustainability challenges. Indeed, to meet these real-world challenges, governments, universities, development agencies, and civil society organizations have made substantial investments in transdisciplinary partnerships. Yet to date, our understanding of the performance, as well as impacts, of these partnerships for sustainability is limited. Here, we provide a guide to assess the performance and impacts of transdisciplinary partnerships for sustainability. We offer key steps to navigate and examine the partnership process for continuous improvement, and to understand how transdisciplinary partnership is contributing to sustainable futures.
Contextual evaluation of multi-, inter-, and transdisciplinary research
in: Bernard Hubert et Nicole Mathieu et al. (Eds) Interdisciplinarités entre Natures et Sociétés, Peter Lang, 2016
Most evaluation procedures that are presently in use remain based on more traditional views of the relationship between science and society, views dominated by monodisciplinary perspectives and linear models. In these, assessment focuses on the output of scientific research as measured by indicators that regard publications in high impact journals, and patents in some case. It is well known that these methods don’t fit most of the social sciences and humanities, but they also are not suitable for a lot of other fields such as engineering and design, law, public health, agricultural and food research. A lot of the fields mentioned here almost as a rule work together with other disciplines, with industry (big and small) or with stakeholders in society. Also, these traditional methods are not appropriate to multi-, inter- or transdisciplinary research (from now on: MIT research). In this article, we will argue that other evaluation approaches are needed that take into account the interactions between the different parties in multi-stakeholder networks. We refer to that kind of evaluation as contextual evaluation
Indicator development as 'boundary spanning' between scientists and policy-makers
Science and Public Policy, 2009
In sustainability impact assessments the development of widely accepted indicators that structure the subject area and provide the framework for assessing sustainability impacts is clearly important. We argue that the development of sustainability indicators in science-based initiatives works across the science/policy interface where social and natural scientists as well as stakeholders and policy-makers both translate concepts and ideas to each other and thus make value judgments about the selection of indicators and related sub-classes. This explains why pure scientific indicator initiatives based on simple 'knowledge transfer' models are unlikely to succeed. Instead, sustainability indicator development requires a 'knowledge transaction model' which spans the boundary between the scientific and the political domains.