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Record W4386069630 · doi:10.1080/13875868.2023.2250537

Sex differences in self-reported spatial abilities and affect: a systematic review and meta-analysis

2023· review· en· W4386069630 on OpenAlexafffund
Victoria Matthews, Clarisse Ramirez, Kate B. Metcalfe, Madeline Wiseman, Daniel Voyer

Bibliographic record

VenueSpatial Cognition and Computation · 2023
Typereview
Languageen
FieldEngineering
TopicSpatial Cognition and Navigation
Canadian institutionsUniversity of New Brunswick
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAffect (linguistics)ModerationMeta-analysisPsychologySample (material)Set (abstract data type)Social psychologyApplied psychologyMedicineComputer science

Abstract

fetched live from OpenAlex

The present meta-analysis of 559 effect sizes examined sex differences in self-reported spatial abilities and affect, and their potential moderators. Results revealed a mean g of 0.498 (95% CI = 0.468 to 0.528), indicating that, on average, males tend to report better abilities and more positive affect toward spatial tasks than females. The moderating role of age in the overall sample showed that sex differences emerge during adolescence. Moderator analyses separately for each ability or affect dimension showed an effect of age similar to that in the overall sample for spatial anxiety scales. We discuss the implications of the results for a potential role of gender stereotype endorsement, sexual maturity, and experiential factors in self-reported spatial abilities and affect along with suggestions for future research.

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)0.007
metaresearch head score (Gemma)0.019
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.010
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.019
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0100.017
Bibliometrics0.0060.006
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.100
GPT teacher head0.330
Teacher spread0.230 · 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 designMeta-analysis
Domainnot available
GenreReview

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

Citations5
Published2023
Admission routes2
Has abstractyes

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