A Theoretical Framework on Sustainable Supply Chains: Barriers to Measuring Performance
Bibliographic record
Abstract
The direction for the construction of a sustainable supply chain concept has an evolution and contribution of multiple disciplines that have been elaborated by academic and business bias. From this point on, defining a concept of this subject represents an issue that demands an interpretative effort, since several factors and theoretical approaches influence this category. The objective of this article is to demarcate a theoretical framework on sustainable supply chains and relate it to the barriers present in the measurement of sustainable performance. The method applied in this assessment combines systematic literature review, qualitative analysis of content and bibliometrics, through interconnected steps, which allow a detailing of the dimensions and under dimensions of the sustainability in the supply chain and the identification of the barriers that are associated with the measurement of performance. The material considered is supported through theoretical and empirical studies, which approached the formulation of the concepts and their applicability at different levels of the supply chain. This allows the content analysis to demarcate certain stages of development and the different theoretical approaches that respond and assist the concept. The results contribute to the definition of a roadmap to measure of sustainable performance, an issue that is the basis of future studies over this theme.
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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 | 0.052 | 0.082 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.020 | 0.016 |
| Science and technology studies | 0.006 | 0.046 |
| Scholarly communication | 0.020 | 0.030 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".