An Interdisciplinary Inquiry Into Sustainable Supply Chain Management
Bibliographic record
Abstract
Research in the last two decades has broadened venues from optimizing operations for a specific organization to critically examining the entire supply chain from the perspective of sustainability. The term sustainability has been used in varying meanings in different disciplines. With this chapter, the authors propose to bring together an interdisciplinary framework for sustainable supply chain management (SSCM). SSCM will be studied through literature surveys on the axes of both natural sciences, and social sciences, with an overarching goal of policy implications. Unlike quantitatively oriented natural sciences, integrating perspectives from the social sciences into a firm's overall sustainability strategy is still seen as a large undertaking by firms and can impede its sustainability. More practical and scholarly research needs to be conducted in this area, especially in terms of assessment and evaluation mechanisms.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.002 | 0.015 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".