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Record W4401510745 · doi:10.7758/rsf.2022.8.7.08

Psychological Challenges and Social Supports That Shape the Pursuit of Socioeconomic Mobility

2022· article· en· W4401510745 on OpenAlexaff
Mesmin Destin, Régine Debrosse, Michelle Rheinschmidt‐Same, Jennifer A. Richeson

Bibliographic record

VenueRSF The Russell Sage Foundation Journal of the Social Sciences · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsMcGill University
Fundersnot available
KeywordsSocioeconomic statusPsychologySocial mobilitySocial psychologySociologyGerontologyDemographyMedicineSocial science

Abstract

fetched live from OpenAlex

Many people seek higher status through socioeconomic mobility. Higher education institutions and professional workplaces include barriers to entry and inclusion that make it difficult for people from lower socioeconomic status (SES) backgrounds to reach their goals. Experiences within these settings can lead people to feel status uncertainty, which is an aversive ambiguity about where one stands on the socioeconomic hierarchy. Status uncertainty has negative consequences for achievement and well-being, but social support may play a role and buffer against these negative consequences. First, a longitudinal study of college students shows predicted connections between socioeconomic background, status uncertainty, social support, and grades at the end of the college years. Next, an experiment shows that inducing a stronger sense of social support protects against negative workplace outcomes for those from lower SES backgrounds. Together, the studies demonstrate the significance of supportive forces during the pursuit of socioeconomic mobility.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0030.001
Open science0.0000.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.094
GPT teacher head0.386
Teacher spread0.292 · 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations3
Published2022
Admission routes1
Has abstractyes

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Same venueRSF The Russell Sage Foundation Journal of the Social SciencesSame topicHealth disparities and outcomesFrench-language works237,207