Bibliographic record
Abstract
This case expounds on the operating model and achievements of Ant Forest. Ant Forest not only heralded a new model for protecting the environment but also generated unique business value for payments app Alipay and its parent company Ant Financial. Ant Forest is a classic example of how a business can create economic value by delivering social value. In just over three years of operations, Ant Forest chalked up two distinctive achievements: First, it functioned as a green initiative and public benefit platform accessible to any individual, company, public welfare organization, and public benefit corporation (PBC). It offered tangible incentives (planting new trees) to reward low-carbon lifestyles, creating a mutual incentive closed-loop system. Second, it encouraged users to use Alipay by rewarding them with green energy points. Therefore, it delivered a social networking function, a historic key competitive weakness of Alipay, helping to boost the app's usage frequency and user stickiness. It also enabled the creation of personal carbon accounts, a key step in the implementation of Ant Financial's green finance strategy. However, Ant Forest still relied primarily on investments from Ant Financial to survive until early 2020. Zu Wang, Ant Forest's product manager, was keen to overcome this dependence. So, how could Ant Forest become a self-sufficient entity, and how could it build a sustainable environmental protection platform?
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.028 | 0.008 |
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".