Dreaming of a Brighter Future? The Impact of Economic Vulnerability on University Aspirations
Bibliographic record
Abstract
Abstract We investigate how economic vulnerability (measured in terms of poverty and economic insecurity) impacts the educational aspirations of youth, age 12-15, within an inequality of opportunity framework. Using the Canadian National Longitudinal Survey of Children and Youth, we find that poverty is associated with reduced university aspirations from the perspective of the youth (8-9 percentage points) and their mother (11-13 percentage points). Further, poverty incidence matters more than depth. Using the dissimilarity index, circumstances contribute to about 20-25 percent of the inequality in educational aspirations, and a Shapley decomposition of these circumstances suggests that, of this observed inequality, 10-15 percent is due to poverty. Interestingly, economic insecurity is not associated with educational aspirations, and this result persists regardless of how we measure insecurity. This may be due to the fact that, over time, poverty is more likely to persist than economic insecurity. Consequently, while the latter may be seen as a temporary shock, the former may create a feeling of hopelessness, thereby reducing aspirations. Controls for academic effort, including standardized test scores, daily reading, and getting good grades do not impact these findings. Results therefore suggest that alleviating child poverty may improve educational aspirations at a critical time in a youth's life. JEL Codes: I21, I23, I24, I32, D63
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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 | 0.005 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".