Bibliographic record
Abstract
The rising complexity of global supply chains has raised the necessity of sustainable practices integrating economic performance and environmental and social accountability. Though data-centric tools provide great insights, the effectiveness of such systems depends upon the exposition of information to the decision-maker. Human-centered visualization interfaces were found to be the enablers par excellence for user augmentation, enhancement of interpretability, and sustainable decision-making among different stakeholders' groups. The paper discusses the conceptual foundations and the design fundamentals of human-centered visualization for sustainable supply chains through usability, accessibility, and transparency. By integrating sustainability metrics such as carbon footprint, energy efficiency, and waste minimization into user-centric dashboards and interactive programs, organizations can bridge the gap between the complexity of the technique and the depth of human understanding. The conceptual framework presented herein depicts the possibility of tailored visualization strategies ranging from dashboards up through immersive AR and VR systems enabling adaptive planning, real-time monitoring, and informed trade-offs within the parameters of the supply chain operation. Though the present work is conceptual and makes no use of empirical data, it provides an established portal for academicians and practitioners for designing interfaces enabling decision-makers, reducing the barriers of cognition, and increasing trust within sustainability reporting. The paper ends with an outline of the future avenues for investigating personalization, multi-modal interaction, and cross-cultural usability for sustainable supply chain management development.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.002 |
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".