Who gets left behind by left behind places?
Bibliographic record
Abstract
We document that children growing up in places left behind by today's economy experience lower levels of social mobility as adults. Using a longitudinal database that tracks over 20,000 places in the USA from 1980 to 2018, we identify two kinds of left behind places: the 'long-term left behind' that have struggled over long periods of history; and 'recently left-behind' places where conditions have deteriorated. Compared to children of similar baseline household income levels, we find that exposure to left behind places is associated with a 4-percentile reduction in adult income rank. Children fare considerably better when exposed to places where conditions are improving. These outcomes vary across prominent social and spatial categories and are compounded when nearby places are also experiencing hardship. Based on these findings, we argue that left behind places are having 'scarring effects' on children that could manifest long into the future, exacerbating the intergenerational challenges faced by low-income households and communities. Improvements in local economic conditions and outmigration to more prosperous places are, therefore, unlikely to be full remedies for the problems created by left behind places.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".