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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 OpenAlex
Victoria Matthews, Clarisse Ramirez, Kate B. Metcalfe, Madeline Wiseman, Daniel Voyer

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.909
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0040.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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