Between a rock and a hard place: The reasons why women delay childbearing
Bibliographic record
Abstract
The increasing trend for women to delay childbearing is often met with harsh criticism and judgment, based on the assumption that women are prioritizing their careers over having children. An on-line survey of 500 currently childless Canadian women between the ages of 18 and 38 (M = 28) assessed participants’ childbearing intentions and beliefs, and the factors they felt were most important in the timing of childbearing. Although the respondents felt women should ideally have their first child in their late 20s, most expected that they would begin their families in their 30s. The ability to financially support a child was the most strongly endorsed factor in the timing of childbearing, followed by good health, being with a partner who would be an involved and loving parent, and having a proper home in which to raise a child. These findings highlight the values and beliefs that were most salient in participants’ decisions about the timing of childbearing.
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How this classification was reachedexpand
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 | 0.001 |
| 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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".