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Record W4220777686 · doi:10.1017/s0140525x21000455

Improving the generalizability of infant psychological research: The ManyBabies model

2022· letter· en· W4220777686 on OpenAlexaff
Ingmar Visser, Christina Bergmann, Krista Byers‐Heinlein, Rodrigo Dal Ben, Włodzisław Duch, Samuel H. Forbes, Laura Franchin, Michael C. Frank, Alessandra Geraci, J. Kiley Hamlin, Zsuzsa Káldy, Louisa Kulke, Catherine Laverty, Casey Lew‐Williams, Victoria Mateu, Julien Mayor, David Moreau, Iris Nomikou, Tobias Schuwerk, Elizabeth A. Simpson, Leher Singh, Mélanie Söderström, Jessica Sullivan, Marion I. van den Heuvel, Gert Westermann, Yuki Yamada, Lorijn Zaadnoordijk, Martin Zettersten

Bibliographic record

VenueBehavioral and Brain Sciences · 2022
Typeletter
Languageen
FieldPsychology
TopicChild and Animal Learning Development
Canadian institutionsUniversity of ManitobaUniversity of British ColumbiaUniversity of British Columbia HospitalConcordia University
FundersEconomic and Social Research Council
KeywordsGeneralizability theoryExplanatory powerPsychologyLimitingDiversification (marketing strategy)Psychological researchSocial psychologyEpistemologyDevelopmental psychology

Abstract

fetched live from OpenAlex

Yarkoni's analysis clearly articulates a number of concerns limiting the generalizability and explanatory power of psychological findings, many of which are compounded in infancy research. ManyBabies addresses these concerns via a radically collaborative, large-scale and open approach to research that is grounded in theory-building, committed to diversification, and focused on understanding sources of variation.

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)0.114
metaresearch head score (Gemma)0.267
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.886
Threshold uncertainty score0.601

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1140.267
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.001
Science and technology studies0.0030.024
Scholarly communication0.0050.017
Open science0.0050.006
Research integrity0.0140.027
Insufficient payload (model declined to judge)0.0030.002

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 head0.309
GPT teacher head0.450
Teacher spread0.141 · 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.

Study designTheoretical or conceptual
DomainMethods
GenreMethods

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

Citations44
Published2022
Admission routes1
Has abstractyes

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