How Chinese executives view economic challenges and global opportunities
Bibliographic record
Abstract
Purpose To better understand the challenges and opportunities facing China, the IBM Institute for Business Value in cooperation with Oxford Economics surveyed 1,150 executives from across China. Survey respondents represented a variety of industries and included executives from Chinese corporations, start-up enterprises, the government sector and educational institutions. Design/methodology/approach This report shares the executives’ vision for the Chinese economy, and proposes actions to help spark growth and positive change. Findings The Chinese executives surveyed see the current economic environment in China as encompassing five main challenges – immature services sector, declining domestic consumption growth, lending decisions creating over investment in some sectors, declining export growth and environmental issues impacting economic development. Practical implications The article identifies the six most important ways to accelerate China’s growth according to the executives: Originality/value Despite challenges, Chinese executives are optimistic about the country’s economic growth prospects. In fact, 93 percent of executives believe China will maintain stable to high growth of more than 5 percent over the next five years. And almost a quarter of them believe China will be able to return to its recent very high growth rates in excess of 8 percent.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".