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Record W4417411299 · doi:10.71240/lcyc.568858

Reversal or no reversal, that is the question – A conceptual replication of cross-cultural spatial-numerical associations

2025· article· W4417411299 on OpenAlexaboutno aff
Samuel Shaki, Martin H. Fischer

Bibliographic record

Venuenot available
Typearticle
Language
FieldEngineering
TopicSpatial Cognition and Navigation
Canadian institutionsnot available
Fundersnot available
KeywordsReplication (statistics)Reading (process)GermanParity (physics)Object (grammar)

Abstract

fetched live from OpenAlex

Reading direction is a central determinant of spatial–numerical associations, yet evidence from right-to-left (RL) readers remains inconsistent, ranging from weakened to reversed spatial-numerical associations (SNAs). Shaki et al. (2009) provided the only systematic cross-cultural comparison controlling for reading direction of both words and digits. They reported canonical SNAs in Canadian left-to-right (LR) readers, no SNAs in Hebrew readers with mixed habits (RL words, LR digits), and a reversed SNAs in Palestinian adults with consistent RL reading. Subsequent studies produced mixed results for populations with similar reading profiles, potentially reflecting uncontrolled bilingualism, differing directional habits, and culturally shaped spatial routines, such as object counting and finger counting. Given the theoretical importance of a reverse SNAs and the absence of a successful replication of this finding in standard parity tasks, the present study replicates Shaki et al. (2009), using the refined go/no-go methodology while additionally documenting participants’ object- and finger-counting preferences. We will test German LR readers, Israeli mixed-direction readers, and Palestinian RL readers with adequately powered samples (n = 39 per subgroup). This replication will clarify whether and how culturally transmitted directional habits contribute to spatial–numerical mappings.

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 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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.635
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.034
GPT teacher head0.347
Teacher spread0.313 · 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 teacher head, not a consensus.

Study designNot applicable
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

Citations0
Published2025
Admission routes1
Has abstractyes

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