‘Could It Be That They Do Not Want to Hear What We Have to Say?’ Organised Working Children and the International Politics and Representations of Child Labour
Bibliographic record
Abstract
Abstract This chapter is primarily concerned with exploring and problematising the representational strategies deployed in processes of global governance and advocacy related to child labour. It is observed that on the international level the broad category of ‘child labour’ is narrowly represented as a form of modern slavery by the International Labour Organisation (ILO) and influential NGOs such as the Global March Against Child labour. Actors that challenge such narratives, in particular working children’s movements, are excluded from exerting influence. To better understand how we arrived as this impasse and how we can move forward, the chapter aims to reconstruct, analyse and problematise the institutional and geo-political developments that have resulted in the current status quo, with a special focus on the efforts of organised working children to influence this process. Using the theoretical constructs of the ‘paradox of institutionalisation’, the ‘paradox of a children’s right to participation’ and the ‘problematic of representational power’ to analyse the chapter’s empirical reconstruction, it is concluded that the ILO is to reconsider its restrictive advocacy campaign in favour of identifying more nuanced and evidence-based programmes and policies on child labour.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.027 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".