Sooner, later, or never: Changing fertility intentions due to Covid-19 in China’s Covid-19 epicentre
Why this work is in the frame
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Bibliographic record
Abstract
Using survey data collected from Hubei province, China's Covid-19 epicentre, in August 2020, this study examines how fertility intentions of Chinese citizens changed during the Covid-19 pandemic. We consider not only whether people changed their fertility plans due to Covid-19 but also distinguish three types of change: bringing forward ('sooner'), postponing ('later'), and abandoning ('never') planned fertility. Over half of those who planned to have a child intended to change their fertility plans due to Covid-19. Younger individuals, those of non-Han ethnicities, urban residents, those with one child already, and those with ever-infected family members were more likely to change their fertility plans. While the effects of some characteristics seem to be short term, other characteristics such as age and number of children show more consequential influences. Older individuals and those planning their second child were particularly prone to abandoning their childbearing plans due to Covid-19. The pandemic may thus complicate China's latest efforts to boost its low fertility.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it