Strategies generates by ABC to win in domestic and global competition
Bibliographic record
Abstract
In today electronic competition ABC is facing with the challenge to compete with international brand. There is also challenge for PT. SKM as the sole agent of ABC product to strengthen their domestic market share in this tough competition. ABC as the domestic manufacturer, which want to expand their market internationally they have to be more proactive in order to gain their competitiveness. In term of gathering information, the data, and relevant theory process, the writer use both of primary and secondary data research method. The primary data which are used by the writers is interview. The secondary data got by the writer comes from article in the intemet, company?s consultant, textbook and alike. ABC has to concern three problems in terms of winning domestic and global competition. First they have to pay attention to the recent change of new trend in retailing. Second, find the best marketing strategy to be implemented while they try to penetrate Thailand markets. Third, learns fiom the previous failure of expanding market trough find the best expansion strategies. ABC has implement and fmd the best marketing strategies can be implemented both in domestic and global market. By doing so, they also have to choose the best expansion strategies should be implement to expand the market.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.080 | 0.020 |
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".