Competition For Market Share: A Markov Analysis Of U.S. Apparel Imports
Bibliographic record
Abstract
This study employed Markov analysis to investigate changes in market shares held by primary world regions supplying apparel to the U.S. market. Movement in market shares among six world regions providing eighteen categories of apparel were examined for the time period 1989-2001. The East Asian region was found to be most competitive, maintaining the majority of its market share and gaining shares from other regions while, simultaneously, relinquishing little market share to any other region. The Caribbean Basin countries, Canada, and Mexico also tended to be successful in retaining market shares for most categories. ASEAN and South Asian countries exhibited ability to retain relatively high portions of market share in some apparel categories, while losing substantial portions in others. The EU15, the only region consisting predominantly of developed countries, tended to have the greatest difficulty maintaining U.S. market share across all categories. Findings support concepts regarding the relationship between level of overall economic development and stage of development of the textile and apparel industry, as well as use of specialization in production as a means of establishing and maintaining a competitive presence within the global setting. Presented at 12th International Conference in Bangkok, Thailand, May 2002.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".