ENT 530 : Case Study: Trixie Beauty / Izyan Syahirah Lokman ... [et al.]
Bibliographic record
Abstract
This case study is about Trixie Beauty which a business that are selling various kind of The Ordinary products, Korea skincare and more face product. There is many information that we get from the interview with their founder which is Ms. Faten. From the interview, we get to know about their company background, organization structure, marketing, strategy for their business, problem that they face, strength and financial. Trixie Beauty is a small business of online retailer which provide an authentic skincare product from Australia, Canada, United Kingdom, and South Korea. They sell the goods in affordable price in Malaysia market. The strength of Trixie Beauty is they do not face any debt regarding to their business. The main problem that Trixie Beauty faced is time management. Ms. Faten and her friend is full time student, so they do not have more time to spend in their business. For this solution, I recommend them to do timetable to decide how many hours they need to spend in their business. Time management is important for online business because they need time to do packaging and shipping the product. This case study about to measure sustainability of the business owner in facing the problem. It also shows us the important of SWOT analysis to analyze small business and it will be important to us if we want to start a business in the future.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".