Exploring the Role of Social Protection Interventions in Preventing Child Labour: Reinforcing the Case for a Human Rights-Based Model of Intervention
Bibliographic record
Abstract
Abstract Social protection measures have emerged as critical interventions to tackle child labour. However, the effectiveness of divergent models of social protection for preventing child labour is undertheorized by academic scholars, and the specific links between child labour and social protection policy generally are underexplored. To advance knowledge in this field, first, this article develops a conceptual framework to analyse evolving discourses relating to the design of social protection measures adopted by the World Bank (WB) and International Labour Organization (ILO). The analysis distinguishes between minimalist ‘safety-net and market-centred’ approaches to social protection (associated with the WB), and more fulsome ‘human rights-based’ interventions (associated with the ILO). The implications of these diverse models of social protection and their impact on children in economic exploitation are analysed. Second, the article engages in an innovative analysis of available empirical studies to measure the effectiveness in practice of different models of social protection. The article argues that interventions that are explicitly linked to broader socio-economic rights and align with a ‘human rights-based’ approach give rise to the most effective results. In contrast, interventions that adopt a ‘safety-net and market-centred’ approach can result in mixed outcomes, and/or increases in child labour. A further finding from the analysis reveals that the gendered burden of social reproduction work is a structural issue that cuts across all of the different social protection interventions and plays a crucial role in their varying outcomes. The article concludes with recommendations for policy makers that have implications for the design of ‘child-friendly’ social protection.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.059 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.031 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".