Sustainable evaluation and verification in supply chains: Aligning and leveraging accountability to stakeholders
Bibliographic record
Abstract
Abstract Managers are being challenged by multiple (and diverse) stakeholders, which have variety of expectations and informational needs about their firm’s supply chains. Collectively, these expectations and needs form a multi‐faceted view of stakeholder accountability, namely the extent to which a firm justifies behaviors and actions across its extended supply chain to stakeholders. To date, sustainable supply chain management research has largely focused on monitoring as a self‐managed set of narrowly defined evaluative activities employed by firms to provide stakeholder accountability. Nevertheless, evidence is emerging that firms have developed a wide variety of monitoring systems in order to align with stakeholders’ expectations and leverage accountability to stakeholders. Drawing from the accounting literature, we synthesize a model that proposes how firms might address accountability for sustainability issues in their supply chain. At its core, the construct of sustainable evaluation and verification (SEV) captures three interrelated dimensions: inclusivity, scope, and disclosure. These dimensions characterize how supply chain processes might identify key measures, collect and process data, and finally, verify materiality, reliability and accuracy of any data and resulting information. As a result, the concept of monitoring is significantly extended, while also considering how different stakeholders can play diverse, active roles as metrics are established, audits are conducted, and information is validated. Also, several antecedents of SEV systems are explored. Finally, the means by which an SEV system can create a competitive advantage are investigated.
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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 | 0.064 | 0.112 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".