A Comparison Between the Second-Hand Clothing Consumption of China and Korea
Bibliographic record
Abstract
The purpose of this study is to research and analyze the second-hand platforms in China and Korea. Todays, second-hand trading has evolved and more and more consumers are using it. In the past, most of the users of the second-hand trading market were low-income households, and the current users of the used trading market are the MZ generation. In this reason, feeling the necessity of researching the second-hand trading platform, I analyze it from data of China and Korea. To achieve the purpose of this study, I collect and analyze two big platform of each country “Xianyu” from China and “Secondhand Market” from Korea. We can see how differences in economies, societies and cultures between China and Korea affect second-hand trading platforms. The study compared the trend of second-hand clothing consumption, the major channels of second-hand clothing consumption, the key categories of second-hand clothing consumption, the leading brands of second-hand clothing consumption, the motivations and obstacles of second-hand clothing consumption. I hope this study to develop the second-hand trading platforms around the world.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".