How the Alibaba Group Implements Its Mission in Fighting Against the Epidemic
Bibliographic record
Abstract
The epidemic is one of the topics that cannot be ignored in 2020. It broke out suddenly and spread rapidly all over the world. The Alibaba Group, as a business giant, has performed well in fighting the epidemic. This paper is a case study on strategic management, focusing on the Alibaba Group to discuss and analyze the specific efforts and changes that it has made to fight the epidemic under its mission. By analyzing the financial reports of the last three quarters, which are September Quarter 2019 Results, December Quarter 2019 Results, and March Quarter 2020 and Full Fiscal Year 2020 Results, it can be found that the Alibaba Group responded very positively and made great contributions in the process of fighting the epidemic. Every crucial decision of the Alibaba Group takes the corporate mission as the core, and it is capable and willing to maintain its mission.
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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 | 0.013 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.017 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".