Improvement of the Production Quality of the Textile Industries in Madagascar by the Knowledge Engineering
Bibliographic record
Abstract
The textile industry in Madagascar has a very important weight for the Malagasy economic situation. It is the sector that contributes the most to job creation as well as to export. There are two main categories of factories: free zone companies that are moving towards export and small and medium-sized units who produce for local consumption. The lack of technical competence of the majority of the employees constitutes a common block for the two factories category. The failure is related to the low employee’s education level. This gap questions the competitiveness of textile enterprises in Madagascar at national and global level. Moreover, quality is one of the critical success factors that must be mastered by textile companies to be able to dominate the world of competition. This paper suggests a managerial strategy, the Knowledge management, as lever of quality production improvement. It has as objective the capitalization, enhancement and improvement of the company's knowledge while placing at the center the human resources. These are the sources of knowledge and the challenge is to formalize and share expert is know-how. Nonaka’s model has been exploited to achieve knowledge transfer. MASK method is used to rationalize Nonaka's knowledge management cycle. It is recommended that textile companies in Madagascar integrate knowledge management into their management system in order to optimize production quality, productivity and stimulate innovation.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".