Halal Meat Exports Enhancement of Pakistan: An Intermediating Role of Global Technical Standards in Quality Function Deployment Model
Bibliographic record
Abstract
A lean and sustainable food supply chain is one of the major strategies that businesses all around the world have been trying to adopt to provide the customers quality food and to remain competitive in the global market. Many industrialists, researchers, and economists have focused on food quality because of the high importance of this issue in the global meat markets context. This research was conducted to investigate the mediating role of Global Technical Standards (GTS) on Voice of Customers (VOC) and Exports Enhancement (EE). Halal meat industry and exports of Pakistan were the primary focus by using a mixed methodological approach. Initially, the quality function deployment (QFD) model was generated for the identification of exports requirements and competitive Novelty Analysis. Fourteen actors (experts) of Pakistan halal meat industry had participated in the identification of requirements and standards. Likewise, exporters from nine economies, including the United States of America, Brazil, Australia, Netherlands, Poland, Spain, India, Canada, and Pakistan, participated in Competitive Novelty Analysis. Secondly, 250 responses were generated from Pakistan’s halal meat industry on a five-point Likert scale. The findings of this study show a significant relationship between Voice of Customers (VOC) and Global Technical Standards (GTS). It was further highlighted that Global Technical Standards mediate the relationship between issues in exports and Exports Enhancement (EE) strategies.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".