The Impact of the Covid-19 on Real Estate Companies: Vanke A and Poly Development
Bibliographic record
Abstract
The COVID-19 pandemic has had a profound impact on the global macroeconomic landscape, social order, and corporate operations. In light of the prevailing trend of economic globalization, the pandemic has significantly affected this process, but it will not deter the progress of economic globalization. The COVID-19 pandemic has not only had a considerable impact on various countries worldwide but has also affected China. To protect the lives and health of our citizens, China has implemented strict containment policies. The pandemic has also had a significant impression on company operations. Our research focuses on two major Chinese real estate companies, "Poly Developments" and "Vanke A." Both companies have publicly available and transparent financial reports that include information on profit figures, profit margins, cash flow, and debt situations. Despite the impact of the COVID-19 pandemic, Vanke witnessed a downward trend in total revenue and cash flow from 2019 to 2021, but a slight recovery was observed in 2022. Vanke displayed stable revenue growth in its development activities and gradually stabilized its profitability. The company also prioritized effective cash flow management and pursued a diversified development strategy, leading to a significant year-on-year increase in investment cash flow. Overall, Vanke exhibited remarkable performance in sustainable strategic planning, collaboration, and operational activities, maintaining its position as an industry leader.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".