Development of a parametric matrix based on GSCM literature
Bibliographic record
Abstract
Today Green Supply Chain Management (GSCM) still remains as an attempt alone. For more than a decade, supply chain evolution to be a low carbon chain in an energy and resource constrained world has been posing greater challenges. Though environmental policies remain without much variation towards green operations in an industry, the implantation strategies and stages vary. There have been a number of studies by researchers and a lot of endeavors by organizations to build a green supply chain, mainly because of pressure from the professional bodies like EPA, WEEE, OECD, and Clean Air Act to reduce emissions with growing concerns over climate change issues, global warming, and requirements from policy makers, end users, stake holders and others. But, at the implementation level there lacked a proper framework on GSCM which suggests guidelines for proper planning and coordination, and the practices to be adopted. This indicates the need for a complete strategic approach on the part of decision makers across the supply chain to have sustainability as a corporate social responsibility. The research gap exists in a holistic perception with regard to a product when supply chain stages are mapped for sustainability due to diverse environmental issues. Also, to build a holistic approach, we must know how the stages and levels interact. Thus, in this work, a Parametric Matrix demarking various level and stages of a supply chain related to a product, in general, is developed from various literatures till date. Such a metric will be useful for the industries to identify the bottleneck areas in their supply chain towards sustainability. Hence, the desired and relevant level of factors to be considered at each stage to become green can easily be decided.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 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".