A Comparison Between the Second-Hand Clothing Consumption of China and Korea
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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