Motivational Accounts of the Vicious Cycle of Social Status: An Integrative Framework Using the United States as a Case Study
Bibliographic record
Abstract
Social mobility is limited in most industrialized countries, and especially in the United States: Children born to relatively poor parents are less likely to prosper than other children. This observation has multiple explanations; in the current article, we focus on emerging motivational perspectives, synthesizing them into a novel integrative framework grounded in a classic theory of motivation: expectancy-value theory. Together, these findings indicate that individuals with lower socioeconomic status (SES) may be less motivated to achieve status relative to individuals with higher SES-not because of their own personal failings, but as a result of their material, social and cultural contexts. We then consider the significant theoretical advantages of this integrative framework, most notably that it enables us to consider how the disparate perspectives linking motivation to SES are linked and may at times compound or offset each other. In turn, this enables us to make sophisticated predictions concerning the conditions that will enable individuals with low SES to escape the vicious cycle of low motivation. Moreover, our account helps bridge the gap between explanations that locate the cause for low social mobility within individuals and those that locate it in the broader system. We end by addressing implications for the psychological understanding of low status and implications for social policy.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".