Shaped by place? Young people's aspirations in disadvantaged neighbourhoods
Bibliographic record
Abstract
This paper aims to better understand the relationship between young people's aspirations towards education and jobs, and the context in which they are formed, especially to understand better the role of disadvantaged places in shaping young people's aspirations. Policy makers maintain that disadvantaged areas are associated with low aspirations and there is support for this position from academic work on neighbourhood effects and local labour markets, but evidence is slim. Using a two-stage survey of young people in disadvantaged settings in three British cities, the paper provides new data on the nature of young peoples’ aspirations, how they change during the teenage years, and how they relate to the places where they are growing up. The findings are that aspirations are very high and, overall, they do not appear to be depressed in relation to the jobs available in the labour market either by the neighbourhood context or by young people's perceptions of local labour markets. However, there are significant differences between the pattern of aspirations and how they change over time in the three locations. The paper then challenges assumptions in policy and in the literature that disadvantaged places equal low aspirations and suggests that understanding how aspirations are formed requires needs a nuanced approach to the nexus of class, ethnicity and institutional influences within local areas.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".