Supply Chain Structure as a Critical Driver of Sustainable Supplier Practices
Bibliographic record
Abstract
As technology improves the transfer of information, a broader range of customers and stakeholders gain access to more information about what happens within supply chains. As a result, issues like poor worker conditions in suppliers’ facilities are increasingly pushed into the limelight. What used to be hidden behind long distances and language differences is more visible (Lee, 2002; Van Der Zee & Van Der Vorst, 2005). As a result, consumers, governments, and nongovernmental organizations (NGOs) are demanding that companies be held more accountable for what happens. Concerns include the use of sweatshop labor, the provision of safe working conditions, and the payment of a living wage to their employees. In response, a growing number of firms are exploring how to identify, assess, and monitor supplier-related social issues and practices. They can monitor their suppliers to ensure adherence to social expectations, conduct audits, or use a certification provided by an independent third-party. Fairtrade (Fairtrade, 2007) is one such thirdparty certification for agricultural commodities such as coffee and cocoa beans. Following an audit, certification is granted to cooperative farms in developing countries that adhere to a number of sustainability-related principles, including safe working conditions for employees, payment of fair wages, and environmentally friendly cultivation techniques. In contrast, other firms choose to develop their own standards internally, for example Starbucks’ system for assessing and working with farmers, termed Coffee and Farmer Equity (CAFE). These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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.
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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 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".