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Record W4304182846 · doi:10.1002/icd.2379

Who is a thinker? With age, higher <scp>SES</scp> American children increasingly associate social status with divisions in labour

2022· article· en· W4304182846 on OpenAlexaff
Shaylene E. Nancekivell, Tatyana Farrow, Brian A. Maurer

Bibliographic record

VenueInfant and Child Development · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicEarly Childhood Education and Development
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsPsychologyExploratory researchWork (physics)Developmental psychologySample (material)Intellectual developmentSocial psychologySociologySocial science

Abstract

fetched live from OpenAlex

Abstract This exploratory report investigates how children, aged 6‐ to 12‐years, reason about divisions in labour. It focuses on understanding when in development children might associate higher status groups with intellectual as opposed to physical labour. It explores this question by introducing a sample of mostly mid/high‐SES American children to a novel factory setting and then asking them who is likely to have one of two jobs: a ‘builder’ (physical labour), or ‘thinker’ (intellectual labour) job. Older children were more likely than younger children to associate an individual's higher social status with intellectual labour work as opposed to physical labour work. Children also explained their reasoning, and with age their explanations focused more on social factors like the role of access to ‘choices’ or opportunities in shaping the nature of others' work.

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 score0.019
Threshold uncertainty score0.037

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 communication0.0020.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.008
GPT teacher head0.250
Teacher spread0.242 · 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

Citations2
Published2022
Admission routes1
Has abstractyes

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