Bibliographic record
Abstract
This case illustrates how ATRenew used digital technology to create a transaction and service platform for second-hand 3C products. ATRenew was established to " Give a second life to all idle goods.” It focused initially on consumer electronics recycling (evidenced by its launch of the Aihuishou C2B platform in 2011) before venturing into the B2B business in 2017 via PJT Marketplace (a platform that aims to connect second-hand buyers and sellers) and the B2C business in 2019 by merging with Paipai, JD.com’s re-commerce arm. When combining the C2B, B2B, and B2C business into one integrated platform, the company developed standard quality inspection processes, a rating system for recycled products (including mobile phones and other 3C products), as well as a C2B and B2B pricing models that considered ratings, and built operations centers and a supply chain to best serve its entire business ecosystem. These efforts also empowered its partners to get the most out of second-hand items. However, as the company grew, leading one-stop re-commerce platforms, typically highly trafficked, broke into the second-hand 3C business to divide up the pie. In May 2022, the company’s management revisited the trade-off between the advantages of competing within existing market space and the advantages of developing new business for the company to achieve lasting success.
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.
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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.021 | 0.032 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.041 | 0.017 |
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".