Economic disadvantage and math achievement: The significance of perceived cost from an evolutionary perspective
Bibliographic record
Abstract
BACKGROUND: Children growing up in poverty tend to perform worse in school than their more economically advantaged peers. AIMS: The current study integrates an educational theory of motivation and an evolutionary theory of life history strategies to examine how economic disadvantage predicts children's mathematics achievement through their academic beliefs and values. SAMPLE: = 12.88) in a large metropolitan city in the United States. METHODS: Economic disadvantage was assessed via school reports of the student being eligible to receive free or reduced-price lunch during the 2014-2015 school year (i.e., at or below 185% of the federal poverty line). Students reported on their perceived interest, usefulness, and cost of learning mathematics during the first half of the 2015-2016 school year (August to December). Mathematics achievement for both school years was assessed via school reports of mathematics grades. RESULTS: Children receiving free or reduced-price lunch showed higher perceived cost of learning mathematics, and this in turn predicted changes in mathematics achievement over time, indirect effect = -0.57, 95% CI (-0.97, -0.23). However, neither interest nor perceived usefulness or ability in mathematics mediated the association between economic disadvantage and changes in mathematics achievement. CONCLUSIONS: Results underscore the potential for interventions to target students' perceived cost of learning as a way to increase school engagement, particularly among disadvantaged students.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".