Helping Working Children through Consumocratic LawA Global South Perspective
Bibliographic record
Abstract
Abstract On the basis of an in-depth case study of a transnational governance scheme driven by consumers and designed to fight child labour in Southern Asia – RugMark (now GoodWeave), co-founded by Peace Nobel Prize recipient Kailash Satyarthi – we identify and describe a number of characteristics peculiar to the consumocratic system of regulation, before examining the impact of information transparency within it. A number of theoretical scenarios emerge from the identification of four critical factors in the regulation of the societal information shared with consumocrats: (1) the degree of subjective veracity and comprehensiveness of available information; (2) the more or less comforting nature of this information; (3) the culminating outcomes to which it refers; and (4) its degree of objective veracity. All societal information is not theoretically bound to convey comforting messages to consumers inclined to consider the well-being of others. This message, while accurately reflecting the results achieved by local consumocratic organisations, may as well reflect the more or less controversial choices made in the pursuit of desirable goals, such as improving the fate of working children. It could also shed light on the flaws (e. g., ethical, technical, managerial) of this system of regulation by exposing its own limits to a better informed public. By opting for the transmission of messages subject to public controversy or worth a mea-culpa, the local regulators of this information would inevitably confront some risks (e. g., judicial, economic, socio-organisational). Under which conditions could these risks be reasonably taken? Quid of their likely impact on altruistic dispositions? From a pragmatic and Global South perspective, a non-paternalist analysis of transparency as a regulatory tool, it is shown, leads to recognising the utility of repositioning consumocratic activity on original, constitutional foundations, before envisaging the development of increasingly transparent and efficient tools in this regard.
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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 | 0.004 | 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 | 0.007 | 0.019 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".