Political instability and sustainable green supply chain management
Bibliographic record
Abstract
In this paper, the relationship between political instability and sustainable supply chain management is examined within the context of Palestine. This is due to the fact that instable state has created a chaotic situation for manufacturers. This paper tends to this subject through quantitative measures from the perspective of employees. A number of 265 employees were selected in manufacturing organizations of Palestine, in which sus-tainable supply chain practices were found to be in the mindset of managers and have been shared with em-ployees and/or the practices are implemented within the firm. This study uses statistical analyses (ANOVA and Correlation analysis) alongside construct reliability and factor loadings to test proposed hypotheses. Results of the analysis show a significant negative effect from political instability and its dimension upon sustainable supply chain management practices and their implementation that is perceived by the employees. Contributions of this papers are threefold as the literature lacks direct examination of current factors, instable countries such as Palestine have not been examined in terms of sustainable supply chain, and managers can benefit from importance of various factors of sustainable practices of 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".