Identification of the Factors Influencing the Cosmetic Products Market (Ukraine Case)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract The global cosmetics market is dynamic and significant in size. Cosmetic products’ market is constantly expanding its influence to different target audiences and covers all classes of consumers. Marketing strategy of cosmetics corporations exists on different levels: main (global) and adapted (for the region or definite country). Generally, it connects with different influencing factors. Based on this, the main aim of this study is to identify and evaluate the key factors globally and evaluate the same for the Ukrainian market. To collect data about global tendencies authors accumulated existing statistical data, annual reports and scientific papers on this topic. For receiving results and collecting data about Ukrainian consumers, close-ended questionnaires were used as a method of collecting preliminary information. Results were classified, most important key success factors were highlighted and then machine learning techniques were used to provide an analysis of correlation. Our results demonstrated that despite the general difference of financial well-being of consumers in USA, Canada and European countries, Ukraine does not differ in consumer preferences by price, as a main factor. For sure it should be noticed, that price is the most influential in third world countries, but Ukrainian market has its own more influential specific factors.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it