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Record W3086298718 · doi:10.1108/jes-12-2019-0562

Sibship size and educational attainment of Canadian baby boomers

2020· article· en· W3086298718 on OpenAlexaffabout
Maryam Dilmaghani

Bibliographic record

VenueJournal of Economic Studies · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicIntergenerational Family Dynamics and Caregiving
Canadian institutionsSaint Mary's University
Fundersnot available
KeywordsBaby boomEducational attainmentBaby boomersSiblingDemographyDemographic economicsPsychologyEconomicsDevelopmental psychologySociologyPopulation

Abstract

fetched live from OpenAlex

Purpose The present study assesses how sibship size affects child quality as measured by educational attainment. Design/methodology/approach The data are from the Canadian General Social Surveys (GSS) of 1986, 1990, 1994 and 1995. The sample is restricted to the individuals born in Canada between 1946 and 1965, that is, the baby-boom generation. In addition to controlling for parental education, the sibship size is instrumented by a non-binary variable created based on the sex composition of the sibship. While most previous studies have pooled both genders, the present paper produces by gender estimates Findings The OLS estimates are statistically significant, negative and moderately large for both male and female baby boomers. When the sibship size is instrumented, the estimates indicate that one additional sibling had reduced the educational attainment of male baby boomers by almost half a year. No causal effect for the sibship size is found for female baby boomers. Originality/value This is the first paper on the effects of sibship size on educational attainment, using Canadian data.

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 score0.067
Threshold uncertainty score0.134

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 communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.046
GPT teacher head0.316
Teacher spread0.269 · 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
Published2020
Admission routes2
Has abstractyes

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