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Record W2993148762

Do youth from lower- and higher-income families benefit equally from postsecondary education?

2019· article· en· W2993148762 on OpenAlexaboutno aff
Marc Frenette

Bibliographic record

VenueAnalytical Studies Branch Research Paper Series · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicUrban, Neighborhood, and Segregation Studies
Canadian institutionsnot available
Fundersnot available
KeywordsPostsecondary educationEarningsOutreachAmerican Community SurveySurvey of Income and Program ParticipationFamily incomeDemographic economicsEarned income tax creditHigher educationPsychologyBusinessDemographyEconomicsPovertyEconomic growthSociologyCensusAccountingPopulation
DOInot available

Abstract

fetched live from OpenAlex

It has been well-documented that postsecondary graduates, on average, earn considerably more than others. Consequently, increasing postsecondary enrollment among youth from lower-income families—through targeted student aid or community outreach programs—may constitute an effective mechanism for promoting upward income mobility. However, there currently exists no evidence of the benefits of a postsecondary education (PSE) for youth from lower-income families per se. Using postsecondary administrative records and income tax records, this study bridges this information gap by estimating the association between earnings and PSE by level of parental income among a cohort of Ontario postsecondary graduates and a comparison group of Ontario youth who did not enroll in a postsecondary institution.

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)0.003
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 score0.409
Threshold uncertainty score0.814

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.000
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 head0.085
GPT teacher head0.395
Teacher spread0.310 · 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
Published2019
Admission routes1
Has abstractyes

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