Temporary Aid for Needy Families as family policy for first time mothers
Bibliographic record
Abstract
Abstract Objective This brief report takes a life course approach to describe how first‐time mothers with low incomes participate in Temporary Aid for Needy Families (TANF) before and after birth. Background By providing cash assistance to low‐income mothers with children, TANF functions as a major family policy. Method Population data from a cohort of all births in Oregon across 2016–2017 are linked to TANF participation and employment histories. Centering on the birth event, the study window spanned 24 months before and after the first birth. Multivariate models are used to predict TANF participation around birth. A combination of sequence and cluster analyses illuminate within‐group patterns. Results Around one‐quarter of low‐income mothers relied on TANF at any time in the two‐year study window with about 70% of those participating in TANF during the 6 months after birth. The most common trajectory pattern (41%) was one of TANF enrollment around birth with high likelihood of exit by 6 months following birth, suggesting TANF may function as a short‐term substitute for paid work, that is, paid leave. Other trajectories were characterized by timing of enrollment (prenatal or postnatal) and duration of participation. Clusters with longer participation were comprised of mothers who were young, single, and with less labor market attachment. Conclusions The majority of low‐income single mothers who rely on TANF around birth participate in short spells and exit the program within 1 year. As more states implement paid family leave policies, low‐income mothers who previously enrolled in TANF may opt for paid leave.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".