MétaCan
Menu
Back to cohort
Record W4383376411 · doi:10.31234/osf.io/ztdjn

How Does Culture Evolve?

2023· preprint· en· W4383376411 on OpenAlexaff
Liane Gabora

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicLanguage and cultural evolution
Canadian institutionsUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
Fundersnot available
KeywordsCreativityImitationCognitive scienceCognitionSociocultural evolutionProcess (computing)SociologyPsychologyEpistemologyModularity (biology)Computer scienceSocial psychologyAnthropology

Abstract

fetched live from OpenAlex

This chapter synthesizes evidence from cognitive science, anthropology, psychological studies, and computational models for a complex systems inspired theory of creativity, and its role in cultural evolution. Creativity is guided by the global shape of one’s integrated network of memories, concepts, and beliefs: one’s worldview. This integrated structure and its dynamical change over time are described using autocatalytic networks. Autocatalytic networks can interact with each other, and they can grow and evolve; through interactions between their components, they generate novel components. Thus, they are used to describe cultural change both within and between individuals, as well as across cultural lineages. The chapter outlines autocatalytic network models of the origin of culture, the cognitive developmental process by which each child becomes a participant in cultural evolution, and the role of imitation, leadership, and social media on cultural evolution, as well as the trade-off between creativity and continuity.

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)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0030.014
Scholarly communication0.0110.014
Open science0.0010.004
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0050.001

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 head0.038
GPT teacher head0.322
Teacher spread0.283 · 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 designTheoretical or conceptual
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
Published2023
Admission routes1
Has abstractyes

Explore more

Same topicLanguage and cultural evolutionFrench-language works237,207