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Child Labour: Action-Research-Innovation in South and South-Eastern Asia (CLARISSA) is an evidence and innovation-generation programme funded by the United Kingdom’s Foreign, Commonwealth & Development Office (FCDO), responding to the challenge of the worst forms of child labour (WFCL) in Bangladesh and Nepal. It is a challenge characterised by a poor understanding of its drivers and a lack of evidence on what works to combat it. To handle such fundamental uncertainty, the programme adopts a...
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CLARISSA (Child Labour: Action-Research-Innovation in South and South-Eastern Asia) has a participatory and child-centred approach that supports children to gather evidence, analyse it themselves and generate solutions to the problems they identify. The life story collection and collective analysis processes supported children engaged in the worst forms of child labour in Bangladesh to share and analyse their life stories. Over 400 life stories were collected from children who worked in...
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The CLARISSA Social Protection Intervention was set us as an innovative social policy intervention for tackling social ills, with a...
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Audience This guidance is primarily aimed at development and humanitarian professionals who design or manage monitoring and evaluation processes to support design and implementation of qualitative monitoring using Most Significant Change (MSC), Outcome Harvesting (OH), or Outcome Mapping (OM), which are participatory, outcome-focused qualitative methods. Purpose This guidance is a resource to strengthen qualitative monitoring, specifically addressing commonplace gaps or weakness in the...
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This Research and Evidence Paper presents the theory-based and participatory evaluation design of the Child Labour: Action-Research- Innovation in South and South-Eastern Asia (CLARISSA) programme. The evaluation is embedded in emergent Participatory Action Research with children and other stakeholders to address the drivers of the worst forms of child labour (WFCL). The report describes the use of contribution analysis as an overarching approach, with its emphasis on crafting, nesting and...
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Doing development differently rests on deliberate efforts to reflect and learn, not just about what programmes are doing and achieving, but about how they are working. This is particularly important for an action research programme like Child Labour: Action- Research-Innovation in South and South-Eastern Asia (CLARISSA), which is implemented by a consortium of organisations from across the research and development spectrum, during a rapidly changing global pandemic. Harnessing the potential...
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CLARISSA (Child Labour: Action-Research-Innovation in South and South-Eastern Asia) is a large-scale Participatory Action Research programme which aims to identify, evidence, and promote effective multi-stakeholder action to tackle the drivers of the worst forms of child labour in selected supply chains in Bangladesh, Nepal, and Myanmar. CLARISSA places a particular focus on participants’ own ‘agency’. In other words, participants’ ability to understand the situation they face, and to...
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This article explores the potential of using participatory action research as an adaptive programming modality to drive learning and innovation to tackle the drivers of (and seek to eliminate) the Worst Forms of Child Labour. We draw on our experience from early phases of implementation of a large-scale action research programme, which despite the constraints covid-19 posed in moving to full implementation and participatory engagement with children and other stakeholders on the ground, is...
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Children end up in child labour as a result of many, often unknown or hidden, interactions between multiple actors and multiple factors within households, communities, and labour systems, leading to unpredictable outcomes for children and other sector stakeholders and sometimes resulting in the worst forms of child labour (WFCL). It is a complex problem, and interventions aimed at tackling it are also, inevitably, complex and challenging. The way they influence change is non-linear,...
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