Internationalization Promotion Policies in the Halal Food Industry: Comparison of China (Ningxia) and Malaysia
Bibliographic record
Abstract
It is widely recognized that firms seeking to internationalize their activities have to face obstacles and deal with many uncertainties. There is also a broad consensus to the effect that public institutions can mitigate these barriers and facilitate the internationalization process. But it is still quite difficult to measure the exact efficiency of these policies, and it is unclear exactly which export promotion measures are more suitable and adapted to the specific barriers found in a given industry, in our case, the halal food industry. Based on a thorough field research in the halal food industries of the Ningxia Autonomous Hui Region of China and Malaysia, the present article shall explicitly examine how different public agencies can promote and ease access of local firms to international markets. Using both qualitative and quantitative methodologies, the present article will allow us to formulate general conclusions regarding the relationship between firm internationalization and public policies, as well as useful and specific insights for other halal food industries.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".