Surviving Chinese Competition in a Post-Multi-Fibre Arrangement World
Bibliographic record
Abstract
Mauritius is a small island economy where textile and clothing firms were previously benefiting from preferential access to the EU and the US markets under the Multi-Fibre Arrangement (MFA) which imposed limits on the exports of big exporters. With the phasing out of the MFA, Mauritian textile and clothing firms now have to compete with Asian drivers, especially China, without any artificial support. This article studies post-MFA stabilization in a sample of clothing firms in Mauritius. Data are collected by means of face-to-face interviews with the senior management of 20 firms. Findings indicate that the firms have been involved in a series of changes after the phase out of the MFA. These changes include investment in technology, marketing strategies, production reorganization, rationalizing and closing down units of production, and increased specialization. Evidence also indicates that the competitive edge of Mauritian firms lies in their ability to accept small orders with short lead times, and delivering good quality on time. Institutions have also played a major role in ensuring the survival of the clothing industry through industrial policy and relevant support structures.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".