Bibliographic record
Abstract
Scholars often attribute these sentiments to widespread economic distress found in many rural communities (Monnat and Brown 2017).Some rural areas have experienced deep poverty persisting across generations (Lichter and Johnson 2007;Thiede, Kim, and Valasik 2018), whereas others have witnessed steady economic decline over the last fifty years as a consequence of economic restructuring (Lobao Growing Up in Rural America Shelley Cl a r k, Sa m h a r per, a nd BruCe W eBerThis article examines the context of growing up in rural America and how rural roots shape life chances.The distinctive physical, social, and cultural attributes of rural areas can exacerbate many of the challenges of childhood poverty.Yet rural children have better access to public childcare services and perform as well as urban children on standardized tests.Life trajectories diverge most sharply when rural youths decide whether to leave their home communities.Those who stay typically face limited opportunities for higher education and well-paid, stable employment, whereas those who leave fare remarkably well with respect to their educational, economic, and health outcomes.In sum, growing up in rural America offers distinctive advantages and disadvantages, yet the benefits may accrue primarily to those who 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 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".