A complex systems model for transformative supply chains in emerging markets
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose Corporations operating global value chains must grapple with a multiplicity of ethical and practical considerations, most notably when value chains extend to emerging markets. Such contexts involve interactions with diverse stakeholders who possess the ability to impact supply chain performance, but who also bring conflicting needs, values and interests. The purpose of this paper is to outline a transformative model of supply chain fairness, arguing that adopting plural fairness principles and practices generates a higher fairness equilibrium which includes all affected stakeholders in the production of fairness outcomes, with consequent positive organizational and system level impacts. Design/methodology/approach Through a philosophically informed overview of the literature on organizational fairness, the paper applies fairness to the management of supplier relations to identify the institutional features of ethically sustainable supply chains. The proposed conceptual model uses a complex adaptive systems approach (CADs), supplemented by describing the contribution of fairness norms and practices. Findings This paper argues that a transformative approach to supply chain fairness can suggest new structures for interaction between firms, stakeholders, mediating institutions and governments. Originality/value Emerging market supply chains are facing significant changes. Adopting a complex adaptive systems perspective upon stakeholder relationships, this paper offers insights from the theoretical literature on fairness, and proposes a normative model of supply chain fairness which accounts for both the normative and empirical aspects of relational complexity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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 it