Mitigating Tannery Pollution in Sub-Saharan Africa and South Asia
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The global leather market is worth more than $270 billion annually, and provides an important and accessible source of manufacturing exports for countries in the Global South. Leather is the source for a range of apparel items, including handbags, belts, shoes, wallets, gloves, and various other products, such as furniture, car seats, and luggage. Behind all leather goods is the tannery industry, with much of the raw materials processing located in the Global South (Lund-Thomsen, 2009, Journal of Business Ethics, vol. 90, p. 57). Unlike most synthetic fibers, which are derived from plastics and associated with the petrochemical industry, leather has the potential for a comparatively lighter footprint because it is based on natural and renewable materials not associated with the carbon emissions of fossil fuels. However, leather has suffered from various concerns, including animal rights and toxic effluents. It is ranked as the fourth most dangerous global industry to human health, with many tanneries in the Global South lacking basic protection for the workers and leaching toxic chromium into rivers (Green Cross and Pure Earth, 2016, World’s worst pollution problems: The toxics beneath our feet). This article explores the prospects for reducing the environmental footprint of tanneries in the Global South, focusing on the Sustainable Manufacturing and Environmental Pollution (SMEP) program, a series of projects in South Asia (SA) and Sub-Saharan Africa (SSA) that explore ways to reduce manufacturing pollution. The article lays out a series of technical and managerial interventions that would vastly reduce the negative impacts on human health and the natural environment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it