The impact of Covid-19 on unethical practices in global supply chains
Bibliographic record
Abstract
As a result of the disruptions caused by Covid-19, this research aims to understand the current state of global textile supply chains, and what impacts the pandemic has caused to the risks of modern slavery and labour exploitation. By taking a wide scope of literature and surveys from multiple stakeholders including: industry, governmental, international, business membership, and non-governmental organisations, we hoped to gain a broad understanding of the current issues. Our objectives were to assess the various impacts the industry has faced, what actions were taken, and what was the reasoning behind those actions. Furthermore, we wanted to evaluate what had been the result of the actions taken and how had the structure of global textile supply chains affected their resilience to the pandemic. From this we were able to highlight the complexity of the problems currently at play with the industry and the variety of decisions being made to try and combat them, as well as the risk to workers within the system and the unpredictability of how the pandemic will unfold.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".