Can Blockchain Impact Social Sustainability in Global Supply Chains by Enhancing Forced and Child Labour Practises
Bibliographic record
Abstract
<p>Supply chain management is more challenging than ever as governments, communities, and consumers demand greater social sustainability from organizations. Global supply chains have been linked to child and forced labour and this has forced organizations to embrace technology as a viable option in fighting theses atrocities. Blockchain (BC) technology is one such option as it offers a set of features that global supply chains can leverage, namely transparency, security, immutability, and decentralization. This qualitative research explores blockchain adoption in global supply chains in the context of child labour and forced labour violations. The findings signify that blockchain can enhance the transparency of global supply chains, and positively impact child and forced labour practises. A key theoretical contribution emerged, by applying blockchain the transparency acquired, due diligence and collaboration is enhanced. Overall, the research is a further step in understanding blockchain's potential to address child and forced labour in global, which future research can build on.</p>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".