MétaCan
Menu
Back to cohort
Record W2138779638 · doi:10.1162/jinh_a_00303

Offspring Sex Preference in Frontier America

2012· article· en· W2138779638 on OpenAlexaff
Nora Bohnert, Hilde Leikny Jåstad, Jessica Vechbanyongratana, Evelien Walhout

Bibliographic record

VenueThe Journal of Interdisciplinary History · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicDemographic Trends and Gender Preferences
Canadian institutionsNorQuest College
Fundersnot available
KeywordsFertilityDemographyPopulationOffspringParity (physics)Birth rateNatural fertilityPreferenceFrontierSex ratioFamily planningGeographyPregnancySociologyEconomicsBiologyResearch methodology

Abstract

fetched live from OpenAlex

Analysis of the fertility histories of women born between 1850 and 1900, as given in the Utah Population Database (UPDB), reveals the effect of the number, as well as the sex composition, of previous children on birth-stopping and birth-spacing decisions. Specifically, agricultural and Church of Jesus Christ of Latter-day Saints (LDS) households—two sub-populations that might have placed different values on male and female children for economic, social, and/or cultural reasons—showed a distinct preference for male children, as expressed by birth stopping after the birth of a male child and shorter birth intervals in higher-parity births when most previous children were female. Remarkably, women in both the early "natural fertility" and the later contraceptive eras used spacing behavior to achieve a desired sex mix. Although the LDS population had relatively high fertility rates, it had the same preferences for male children as the non-LDS population did. Farmers, who presumably had a need for family labor, were more interested in the quantity than in the sex mix of their children.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.013
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.065
GPT teacher head0.312
Teacher spread0.247 · 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

Citations21
Published2012
Admission routes1
Has abstractyes

Explore more

Same venueThe Journal of Interdisciplinary HistorySame topicDemographic Trends and Gender PreferencesFrench-language works237,207