Bibliographic record
Abstract
Multinational firms face many challenges in extending sustainability practices to their global supply chains. Establishing standards for environmental practices and working conditions for suppliers through codes of conduct, and then monitoring their performance with audits, is the common method used by MNEs. However, this approach has proven deficient in many cases as the suppliers are often not capable or unwilling to make the changes necessary to assure long-term sustainability of their operations. Audits often are insufficient in uncovering practices that do not meet the codes of conduct, and in any case, do not usually reveal if the firm is on a path to continue to improve their sustainability practices. Drawing upon the experiences of firms that have implemented productivity and quality improvement programs in their global supply chains, some implications for how to implement successful sustainability programs can be found. The challenges that MNEs and their suppliers must overcome to achieve this are discussed and suggestions made on how to achieve real sustainability in global supply chains.
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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 | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".