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Record W4404025034 · doi:10.22215/cujs.v3i1.4843

Did I Meet My Expectations? Exploring Whether Teen College Expectations Predict Degree Attainment and Mental Health

2024· article· en· W4404025034 on OpenAlexaff
Lauren Campbell, Kira O. McCabe

Bibliographic record

VenueCarleton undergraduate journal of science. · 2024
Typearticle
Languageen
FieldPsychology
TopicPsychological Well-being and Life Satisfaction
Canadian institutionsCarleton University
Fundersnot available
KeywordsDegree (music)PsychologyMental healthEducational attainmentSocial psychologyPsychiatryEconomics

Abstract

fetched live from OpenAlex

The purpose of this study was to investigate the predictive power of teen college expectations on future degree attainment and mental health outcomes. It also explores the role of personality traits in this process. The data are from a sample of 8,984 U.S. residents who participated in the National Longitudinal Study of Youth – 1997 Cohort (NLSY97). Teens reported on their college expectations through self-reported measures in 1997 or 2001. In subsequent surveys (2008 onward), participants reported on their degree attainment, mental health, and personality traits. Individuals with high college expectations compared to individuals with low college expectations had a more positive personality trait profile and better mental health outcomes. When comparing people who met their expectations with people who did not meet their expectations, there were few differences in mental health. There were no significant interactions between college expectations and degree attainment, controlling for ability, socioeconomic status, and gender.

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)0.002
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.067
GPT teacher head0.348
Teacher spread0.281 · 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 designObservational
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

Citations0
Published2024
Admission routes1
Has abstractyes

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