The effects of environmentalism on fertility in the United States: changing trends and causality
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Environmental issues such as climate change have become major contemporary concerns that affect beliefs and, in some cases, behaviors. We examine whether and how environmentalism may have contributed to the second demographic transition that has driven declining fertility in some parts of the world since the 1970s. Data come from the 2006–2014 and 2016–2020 General Social Survey rolling panels in the USA. We fit Bayesian linear mixed-effects models to estimate the effects of ones’ environmental support, as measured by their support for the government’s environmental spending, on their actual childbearing and beliefs about ideal family size. We found that ones’ environmental support was negatively associated with both outcomes. Specifically, the negative association between environmental support and childbearing was stronger among younger cohorts. Among reproductive-aged women (ages 18–45), their environmental support was negatively associated with the likelihood of new childbearing contemporaneously, but did not predict future childbearing. These findings suggest that environmentalism has largely contributed to the second demographic transition in the USA, while also raising another mechanism that parents may deprioritize the environment to cope with expected childrearing responsibilities.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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