Risk assessment for sustainability on telecom supply chain: A hybrid fuzzy approach
Bibliographic record
Abstract
Telecom supply chain (TSC) research has determined several risk sources can happen for sustainable supply chain management (SSCM) due to their ambiguous nature. However, investigation of these risks is relatively sparse and has primarily been independent with less combinatory research, despite their interrelationships and causality. The present study aims to address that gap by an extant literature review and analysis of relationships among risk factors using a combination of fuzzy approaches. A mixed approach was used, including empirical data from private and government firms in a developing telecom sector. This research finding confirmed that economical dimension risk is major for SSCM in the TSC. In developing countries, it could help telecom service providers in determining which risk factors are critical and those that are crucially significant. As a result, they will be able to more effectively develop strategies focussing on the most affecting risk dimension for SSCM. This is the first study using a hybrid fuzzy approach that analysed interrelationships of risk factors for SSCM. Further, it gives a comprehensive view of risk assessment in the risk management context in the supply chain.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".