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Record W1983821843 · doi:10.1068/p5209

Gender Differences in Perception of Self-Orientation: Software or Hardware?

2004· article· en· W1983821843 on OpenAlexafffund
Luc Tremblay, Digby Elliott, Janet L. Starkes

Bibliographic record

VenuePerception · 2004
Typearticle
Languageen
FieldPsychology
TopicEducational Games and Gamification
Canadian institutionsMcMaster UniversityUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPerceptionOrientation (vector space)SoftwarePsychologyComputer scienceCognitive psychologyHuman–computer interactionNeuroscienceGeometryProgramming languageMathematics

Abstract

fetched live from OpenAlex

We evaluated the contribution of attentional strategy to the perception of self-orientation with and without a body tilt in the median plane. Reinking et al (1974 Journal of Personality and Social Psychology 30 807-811) found that the frame dependence of females on the rod-and-frame test could be mediated by instructions prompting them to focus on internal cues (ie arising from inside of the body). Here, we measured the influence of attentional instructions on the perception of the morphological horizon. Eleven females and thirteen males estimated their morphological horizon in an upright and a 45 degrees body tilt in the median plane under three instruction conditions. All participants first performed without attentional instructions. Then, participants performed under both internal and external attentional instructions. For females, but not for males, perception of morphological horizon was more footward in the supine than in the upright orientation. Although instructions did not eliminate gender differences, internal instructions allowed females to reduce their perceptual bias in the supine orientation.

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)0.004
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 score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.004
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)0.0040.000

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 head0.057
GPT teacher head0.347
Teacher spread0.290 · 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

Citations27
Published2004
Admission routes2
Has abstractyes

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