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Record W2899506745 · doi:10.1177/1745691618788875

Motivational Accounts of the Vicious Cycle of Social Status: An Integrative Framework Using the United States as a Case Study

2018· review· en· W2899506745 on OpenAlexaff
Kristin Laurin, Holly R. Engstrom, Adam Alic

Bibliographic record

VenuePerspectives on Psychological Science · 2018
Typereview
Languageen
FieldSocial Sciences
TopicSocial and Intergroup Psychology
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPsychologyVirtuous circle and vicious circleSocial psychologyCognitive psychologyEconomics

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.124
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.004
Science and technology studies0.0020.012
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.120
GPT teacher head0.529
Teacher spread0.409 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations30
Published2018
Admission routes1
Has abstractyes

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