Innovative approaches to the place-based policies. Adopting collective intelligence for effective development of local public policies
Research output: Contribution to conference - NOT published in proceeding or journal › Conference abstract for conference › Research › peer-review
There have been several attempts and experiments to use collective knowledge in solving public problems or creating public benefits for centuries; for example, compiling Oxford English Dictionary in the 19th century, or conducting The Peckham Experiment to improve health in poor neighbourhoods in London in the early 20th century, as well as similar events throughout the history. As these attempts were time and money consuming, initiations, that required massive public engagement, was rare. More the society has become digitalised, the easier, faster and cheaper has been to reach out many individuals and groups and furthermore, to use collective intelligence methods in improving socioeconomic conditions for local communities; for example, online-offline public platform developed recently by the City of Athens to support the projects for improving city life, or Climate Watch platform in Helsinki that helped to develop “Carbon-Neutral Helsinki 2035 Action Plan” in 2018, and many more similar projects initiated by the local administrations worldwide.
On the one hand, collective intelligence is formed when groups of people interact, collaborate, and compete with one another around the common goal or agenda (Suran et al., 2020). Studies have shown that the collective intelligence approach can better predict right solutions to the community problems than the single individuals, even with the relevant expertise (Hong and Page, 2004). On the other hand, place-based approach in policymaking signifies identification and mobilisation of local potential, resources, and capacities by engaging local communities, institutions, and stakeholders. The recent policy practices have shown that the local development policies are more effective if they are tailored to the specific area needs. Therefore, we are interested in learning how local public authorities can gather collective knowledge by applying collective intelligence methods to develop bottom-up tailored and place-based policies and interventions for improving health, economy and living conditions of local communities (Dankwa-Mullan and Pérez-Stable, 2016).
The main aim of this study is twofold. Firstly, it aims to develop the conceptual framework by combining theoretical rationales of collective intelligence and paradigms of place-based policy approaches. And secondly, while adopting the conceptual framework and combined principles, this study aims to demonstrate its practical application on the case study of Slagelse Municipality in Denmark, which has started an initiative action to apply collective intelligence methods to develop local health policy and action plan. This paper will try to capture policymaking process and identify how effective the collective intelligence methods are to capture local knowledge for better and healthier living conditions locally, as well as solving the health disparity issues in the municipality.
We will use gathered data from the “citizenlab” platform in Slagelse Municipality and NLP model, as well as mixed quantitative and qualitative methods to explore process, pros, and cons of the collective intelligence approach in place-based policy making and planning in the contemporary digitalised local communities.
This study will be divided into five parts. In the first part, we discuss the emergence of place-based approaches in policymaking combined with collective intelligence. In the second part, we describe previous works regarding theoretical frameworks of place-based policy approaches and collective intelligence. In the third part, we conceptualise the principles and framework for combining two different theoretical rationales. In the fourth part, we analyse the case study, based on the conceptual framework and in the fifth part, we provide conclusive remarks and discussion.
|Publication status||Published - 2021|
|Event||GEOINNO: 6th Geography of Innovation Conference - Bocconi University, Milano, Italy|
Duration: 26 Jan 2022 → 28 Jan 2022
|Period||26/01/2022 → 28/01/2022|