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Record W3035713355 · doi:10.1080/09540261.2020.1769289

The salience and symbolism of numbers across cultural beliefs and practice

2020· article· en· W3035713355 on OpenAlexaff
Oyedeji Ayonrinde, Anthi Stefatos, Shadé Miller, Amanda M. Richer, Pallavi Nadkarni, Jennifer She, Ahmad Alghofaily, Nomusa Mngoma

Bibliographic record

VenueInternational Review of Psychiatry · 2020
Typearticle
Languageen
FieldPsychology
TopicParanormal Experiences and Beliefs
Canadian institutionsQueen's University
Fundersnot available
KeywordsSalience (neuroscience)PopulationHumanityEvolutionismSociocultural evolutionSocial psychologySociologyPsychologyEpistemologyAnthropologyDemographyCognitive psychologyPhilosophy

Abstract

fetched live from OpenAlex

Numbers are ubiquitous to modern existence and have evolved with humanity over millenia. They structure, record and quantify human behaviour, spiritual belief systems and the evolution of innovation across all spheres of life. Furthermore, cultural identities and interpersonal expression often have numerical components to them for instance rites of passage, population demography and fiscal measures. The salience of numbers in both historical and contemporary cultural life arguably plays a role in individual psyches and the experience of distress or wellness. This paper illustrates the cultural relativism of numbers through superstition and foreboding to auspiciousness in different societies. As a short hand for the quantification of multiple phenomena in low literacy to high technology populations, rural and urban societies as well as traditional and evolving societies, numbers have and will continue to be core to all cultures as they have from prehistoric to contemporary times.

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)0.003
metaresearch head score (Gemma)0.006
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0030.050
Scholarly communication0.0040.005
Open science0.0010.004
Research integrity0.0010.003
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 head0.025
GPT teacher head0.415
Teacher spread0.390 · 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

Citations8
Published2020
Admission routes1
Has abstractyes

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