Exploring Social Class Background Influences and Social Class Transition Challenges
Bibliographic record
Abstract
Social class background refers to the origins of the self, rooted in both the objective material resources one possesses (e.g., income, education level, and occupational prestige) and subjective perceptions of rank relative to others (Côté, 2024). Similar to other inherent individual characteristics, such as gender and race, social class background is an intrinsic attribute that affects individuals’ perceptions and behaviors (Côté, 2011). For instance, individuals with different social class backgrounds show variations in preference for job autonomy (Fang and Tilcsik, 2022), and strategic risks taking behaviors even after promotion to CEO positions (Kish-Gephart and Campbell, 2015). While behavior differences driven by social class background has received significant attention from scholars, the understanding of its impact on shaping organizational behaviors remains underexplored. This symposium seeks to advance the understanding by examining how individuals’ social class backgrounds influence two critical beliefs/behaviors: the perception of overqualification which is an important belief that shapes job satisfaction and intentions to remain, and AI adoption behavior which has the potential to transform individual work performance. In addition to exploring behavioral differences across social classes, this symposium explores challenges faced by social class transitioners, particularly those moving from lower-class to upper- class positions. For example, individuals from lower-class backgrounds often face cultural mismatches (Stephens et al., 2019) when navigating the independent values (Stephens et al., 2012) and lower levels of prosocial tendencies (Piff et al., 2010) that are typical of higher social classes. This symposium shed light on the psychological distress due to norm discrepancy and psychosocial adjustment related to social capital, experienced by the social class transitioners, and aim to provide insights and strategies to support the well-being of social class transitioners and addressing these challenges. Social Class Background and Perceived Overqualification Author: Sean Martin; University of Virginia Social Class Bicultural Identity Integration (BII) Benefits Social Class Transitioners Author: Mindy Truong; University of California Riverside Author: Andrea Dittmann; University of Southern California Author: Jungmin Lee; Emory University The Role of Social Capital in Navigating College for Students from Lower Social Class Backgrounds Author: Rebecca Carey; Princeton University Author: Priscilla Diaz-Gonzalez; Princeton University Author: Nicole Johnson; Princeton University Echoes of Your Roots: How Social Class Background Shapes Help-seeking Behavior from AI Author: Meng Li; Author: Yao Yao; University of Houston Author: Lai Wei; Boston College
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".