Bibliographic record
Abstract
The purpose of this research is to investigate how sustainability is integrated into supply chain management (SCM). Emphasis is particularly devoted to determining how the sustainability of supply chains may be assessed. Four key objectives were developed to support this purpose: (1) define sustainable supply chain management (SSCM), (2) identify and analyze the published metrics for SSCM, (3) develop a comprehensive approach for assessing sustainability performance at the company level, and (4) develop an integrative sustainability performance framework for the broader context of supply chain. The first two objectives were accomplished through two different sets of in-depth literature reviews. The reviews focused on analyzing the convergences and divergences in the literature on green supply chain management (GSCM), SSCM, and the metrics used in these areas. The reviews helped provide the basis for accomplishing the remaining research objectives. Accordingly, stochastic models for measuring sustainability performance at the company and supply chain levels were developed. This research addresses several important gaps in the literature. As research on the integration of sustainability into SCM continues to expand, it becomes increasingly important to highlight the inconsistencies in the various definitions and metrics used to measure GSCM and SSCM. The lack of reasonably consistent definitions and metrics may lead to confusion regarding the appropriate scope in theory and practice of SSCM initiatives. Exploring the implications of and potential resolutions to the many differences in the published definitions and metrics provide much needed reference points, and further provide the foundation necessary to support the development of scientifically-sound sustainability models. By providing relatively simple and informative measurement, the model developed in Objective 3 presents a unique method of adopting the strong sustainability concept to assessing sustainability at the company level. Furthermore, by providing an original and straightforward analytical approach, the SSCM models developed in Objective 4 are one of the first to explicitly adopt probabilistic approaches for sustainability assessment in the supply chain context. Given their unique ability to accommodate any number of SSCM characteristics, the models can be employed as integrative, multi-dimensional tools for evaluating changes in the sustainability status of a supply chain over time.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".