Growth and happiness in China, 1990–2015
Bibliographic record
Abstract
In the past quarter century China’s real GDP per capita has multiplied over five times, an unprecedented feat. By 2012 virtually every urban household had, on average, a color TV, air conditioner, washing machine, and refrigerator. Almost nine in ten had a personal computer, and one in five, an automobile. Rural households lagged somewhat behind urban, but these same symptoms of affluence, which were virtually nonexistent in the countryside in 1990, had become quite common by 2012. In the face of such new-found plenitude, one would suppose that the population’s feelings of well-being would have enjoyed a similar multiplication. Yet, as will be discussed, well-being today is probably less than in 1990. This chapter, which builds on a prior study, describes the evolution of China’s well-being in the quarter century since 1990 and suggests the likely reasons for the disparate trajectories of subjective well-being (SWB) and GDP per capita (hereafter, simply GDP). The terms subjective well-being, life satisfaction, and happiness are used here interchangeably, and refer to people’s overall evaluation of their lives. The chapter also describes important differences in subjective well-being among various groups in the population and notes some possible reasons for these differences.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".