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Record W2128929436 · doi:10.1177/0192513x13479334

Educational Aspirations, Expectations, and Realities for Middle-Income Families

2013· article· en· W2128929436 on OpenAlexafffund
Laura Napolitano, Shelley Pacholok, Frank F. Furstenberg

Bibliographic record

VenueJournal of Family Issues · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicParental Involvement in Education
Canadian institutionsOkanagan University CollegeUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
FundersSocial Sciences and Humanities Research Council of CanadaRussell Sage Foundation
KeywordsNegotiationGovernment (linguistics)Middle incomeDreamEconomic growthSample (material)Social mobilityPsychologySociologyDemographic economicsEconomicsSocial science

Abstract

fetched live from OpenAlex

Although most Americans agree that postsecondary education is the clearest path to later financial security, many families have trouble saving money to help their children in this process. This article focuses on the struggles of middle-income families as they attempt to negotiate their daily financial realities with their aspirations for their children’s postsecondary education. In particular, the article examines the discord between the high educational aspirations these middle-income families have for their children and their daily financial constraints. We do so by analyzing in-depth interviews with 31 middle-income families living in the greater Philadelphia area. The middle-income parents in our sample are acutely aware of the importance of college for their children’s upward mobility, and they ideally would like to support their children in this pursuit. However, their current financial insecurity, their lack of government support, and the rising costs of college make preparing for this dream increasingly difficult.

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)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0060.002
Scholarly communication0.0020.001
Open science0.0000.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.119
GPT teacher head0.390
Teacher spread0.271 · 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 designNot applicable
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

Citations32
Published2013
Admission routes2
Has abstractyes

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