Feeling at Home in College: Fortifying School‐Relevant Selves to Reduce Social Class Disparities in Higher Education
Bibliographic record
Abstract
Social class disparities in higher education between working‐class students (i.e., students who are low income and/or do not have parents with four‐year college degrees) and middle‐class students (i.e., students who are high income and/or have at least one parent with a four year‐degree) are on the rise. There is an urgent need for interventions, or changes to universities' ideas and practices, to increase working‐class students' access to and performance in higher education. The current article identifies key factors that characterize successful interventions aimed at reducing social class disparities, and proposes additional interventions that have the potential to improve working‐class students' chances of college success. As we propose in the article, effective interventions must first address key individual and structural factors that can create barriers to students' college success. At the same time, interventions should also fortify school‐relevant selves, or increase students' sense that the pursuit of a college degree is central to “who I am.” When students experience this strong connection between their selves and what it means to attend and perform well in college, they will gain a sense that they fit in the academic environment and will be empowered to do what it takes to succeed there.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".