MétaCan
Menu
Back to cohort
Record W4200140844 · doi:10.1037/dev0001286

Replication studies of critical findings from the peer literature: An introduction.

2021· article· en· W4200140844 on OpenAlexfundno aff
William M. Bukowski, Wendy Troop‐Gordon

Bibliographic record

VenueDevelopmental Psychology · 2021
Typearticle
Languageen
FieldHealth Professions
TopicCommunity Health and Development
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of CanadaNational Institute of Food and Agriculture
KeywordsReplication (statistics)PsycINFOPsychologyReplicateData scienceMEDLINEComputer scienceBiology

Abstract

fetched live from OpenAlex

Despite its importance, replication has remained in the background of social development research. The aim of this special section was to elucidate and elevate the role of replication in peer relations research, examining its challenges and its utility for moving the field forward. To accomplish this aim, five sets of researchers undertook identifying an important finding from a widely cited article in the peer literature and tried to replicate its basic results using new data. As a group, the resulting five articles cover a broad range of topics, measures, and methods that are seen in peer research. Four of the five articles provide evidence of replication. This evidence was seen more for basic principles or processes observed in earlier studies than for exact or specific findings. In addition, the authors used varied approaches to replication, highlighting the need to embrace diverse methods when attempting to replicate complicated mechanisms of social development. It is argued that replication efforts should be aimed at identifying basic principles and processes of social development while clarifying the parameters that account for variability across studies in the specific findings. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

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)0.004
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: Empirical
Teacher disagreement score0.452
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.214
GPT teacher head0.561
Teacher spread0.348 · 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

Citations2
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueDevelopmental PsychologySame topicCommunity Health and DevelopmentFrench-language works237,207