To deliver on the agenda of Sustainable Development Goals (SDGs) to ‘leave no one behind’, it is necessary to formulate frameworks and instruments which operationalize this principle of broad inclusion and participation. Ensuring that the ‘marginalized’ and ‘excluded’ individuals and population groups are given visibility and a voice across the full spectrum of international and national development activities, from planning and implementation to monitoring and evaluation, is pivotal towards realizing the principle.
In this context, unpacking the diversity and dimensions of marginalization and exclusion in indicators monitoring is thus imperative. This is because existing framing and articulation of SDG indicators could perpetuate and even aggravate marginalization while at the same time, plays a critical role in monitoring progress towards the achievement of the goals and in informing subsequent policy directions. Understanding the varied characteristics of the marginalized and excluded populations groups would facilitate the formulation of effective and targeted interventions to address the specific forms of marginalization.
To investigate the variations of marginalization and exclusion throughout the course of data processes, a framework of marginalized ‘voices’ was developed, and the following five dimensions of marginalization and exclusion were identified: unknown voices, silent voices, muted voices, unheard voices, and ignored voices.
Thinyane, M. (2018). Engaging Citizens for Sustainable Development: A Data Perspective. United Nations University Institute on Computing and Society.
Thinyane, M. & Kirschke, S. (October, 2019). Data assemblages for enhanced citizen participation in sustainable development. Global Partnership for Sustainable Development Data.
Thinyane, M. & Christine, D. (December, 2019). A Typological Framework for Data Marginalization. United Nations University Institute in Macau.
This research is addressing all issues in the UN Sustainable Development Goals (SDGs):
This project is part of the Small Data Lab.