Bibliographic record
Abstract
Abstract Even when family life gets off to a bumpy start, as it did for many of my interviewees, it can take a turn for the better. For about two-fifths, family life began with serious difficulties, ranging from economic distress to severe parental depression and conflict. But among this group, two-thirds (or a quarter of all those interviewed) believe their homes ultimately became supportive and secure. Dwayne’s family hit “rock bottom” when his father left and the household descended into poverty: Whether it was two parents or one, I just thought life was a struggle. We were at rock bottom, so it could only get better. Early domestic difficulties can take many forms, from a clearly unhappy parent to pervasive marital discord to severe economic uncertainty to neglect, abandonment, and mistreatment. Among those with improving family fortunes, most experienced some combination of these domestic woes early on. By the time they reached adulthood, however, all of these young people had a far more optimistic outlook. When Dwayne was twenty-four, his family seemed “on top of the world, maybe with little minor setbacks, but still on top of the world,” and Josh (whose experiences were introduced earlier) saw his household shift from one saturated with parental hostility and estrangement to a close-knit, supportive home:
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.015 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".