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Record W2787248738 · doi:10.1098/rstb.2017.0059

Variation is the universal: making cultural evolution work in developmental psychology

2018· article· en· W2787248738 on OpenAlexaff
Michelle A. Kline, Rubeena Shamsudheen, Tanya Broesch

Bibliographic record

VenuePhilosophical Transactions of the Royal Society B Biological Sciences · 2018
Typearticle
Languageen
FieldPsychology
TopicChild and Animal Learning Development
Canadian institutionsSimon Fraser University
FundersJohn Templeton Foundation
KeywordsVariation (astronomy)Universality (dynamical systems)EpistemologyPopulationScope (computer science)Context (archaeology)CentralitySociologyEvolutionary psychologyCultural psychologyPsychologyCognitive psychologySocial psychologyGeographyComputer science

Abstract

fetched live from OpenAlex

Culture is a human universal, yet it is a source of variation in human psychology, behaviour and development. Developmental researchers are now expanding the geographical scope of research to include populations beyond relatively wealthy Western communities. However, culture and context still play a secondary role in the theoretical grounding of developmental psychology research, far too often. In this paper, we highlight four false assumptions that are common in psychology, and that detract from the quality of both standard and cross-cultural research in development. These assumptions are: (i) the universality assumption , that empirical uniformity is evidence for universality, while any variation is evidence for culturally derived variation; (ii) the Western centrality assumption , that Western populations represent a normal and/or healthy standard against which development in all societies can be compared; (iii) the deficit assumption , that population-level differences in developmental timing or outcomes are necessarily due to something lacking among non-Western populations; and (iv) the equivalency assumption , that using identical research methods will necessarily produce equivalent and externally valid data, across disparate cultural contexts. For each assumption, we draw on cultural evolutionary theory to critique and replace the assumption with a theoretically grounded approach to culture in development. We support these suggestions with positive examples drawn from research in development. Finally, we conclude with a call for researchers to take reasonable steps towards more fully incorporating culture and context into studies of development, by expanding their participant pools in strategic ways. This will lead to a more inclusive and therefore more accurate description of human development. This article is part of the theme issue ‘Bridging cultural gaps: interdisciplinary studies in human cultural evolution’.

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)0.067
metaresearch head score (Gemma)0.090
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: none
Teacher disagreement score0.067
Threshold uncertainty score0.355

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0670.090
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0040.003
Science and technology studies0.0080.079
Scholarly communication0.0100.029
Open science0.0040.015
Research integrity0.0040.008
Insufficient payload (model declined to judge)0.0030.000

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 head0.091
GPT teacher head0.334
Teacher spread0.242 · 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

Citations153
Published2018
Admission routes1
Has abstractyes

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