“Golden Weeks”: New concepts of leisure in China at the beginning of the 21st Century
Bibliographic record
Abstract
In recent decades, China has demonstrated steady economic growth, which has affected the increase in personal income and the level of material well-being of the population. In 2021, the year of the centenary of the Chinese Communist Party, Chairman of the People’s Republic of China, CCP General Secretary Xi Jinping announced the overcoming of absolute poverty and the creation of the moderately prosperous society in China. It seems relevant against this background to consider changes in the attitude of the state and the population to the quality of life, in particular, to spending free time, leisure and recreation, and their content. As an object of research carried out on the basis of publications in Chinese scientific publications and periodicals, the author chose the so–called “golden weeks” – holidays of seven days in the Chinese New Year, May and October holidays. The changes that have occurred with respect to the people and their holidays during almost a quarter of a century were subject of the analysis. The author comes to the conclusion that state bodies of various levels systematically monitor the impact of the “golden weeks” on all aspects of the society, in their decisions comprehensively take into account economic, political, cultural and educational factors. The above fits into the overall strategy of the state development aimed at advancing to higher standards of the people’s lives. The “golden weeks” have revealed the overall trend for the Chinese to move from satisfying the basic needs of life to striving for the enrichment of spiritual life.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| 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".