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Record W3189381819 · doi:10.1177/0956797621997366

Mind-Body Practices and Self-Enhancement: Direct Replications of Gebauer et al.’s (2018) Experiments 1 and 2

2021· article· en· W3189381819 on OpenAlexafffundabout
Thomas I. Vaughan‐Johnston, Jill A. Jacobson, Alex Prosserman, Emily Sanders

Bibliographic record

VenuePsychological Science · 2021
Typearticle
Languageen
FieldPsychology
TopicMindfulness and Compassion Interventions
Canadian institutionsQueen's University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsPsychologyMeditationCentralityId, ego and super-egoPerspective (graphical)Social psychologySelfSelf-enhancementCognitive psychology

Abstract

fetched live from OpenAlex

Mind-body practices such as yoga and meditation are often believed to instill a “quiet ego,” entailing less self-enhancement. In two experiments, however, Gebauer et al. (2018) demonstrated that mind-body practices may actually increase self-enhancement, particularly because such practices become self-central bases for self-esteem. We conducted preregistered replications of both of Gebauer et al.’s experiments. Experiment 1 was a field study of Canadian yoga students ( N = 97), and Experiment 2 was a multiwave meditation intervention among Canadian university students ( N = 300). Our results supported Gebauer et al.’s original conclusions that mind-body practices increase self-enhancement. Although the self-centrality effects were not clearly replicated in either experiment, we found evidence that measurement and sampling differences may explain this discrepancy. Moreover, an integrative data analysis of the original and the replication data strongly supported all of Gebauer et al.’s conclusions. In short, we provide new evidence against the ego-quieting perspective and in support of the self-centrality interpretation of mind-body practices.

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)0.011
metaresearch head score (Gemma)0.041
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Reproducibility · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.989
Threshold uncertainty score0.061

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.041
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0010.001
Science and technology studies0.0030.002
Scholarly communication0.0020.002
Open science0.0020.002
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0060.001

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 head0.091
GPT teacher head0.468
Teacher spread0.377 · 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 designBench or experimental
DomainReproducibility
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

Citations33
Published2021
Admission routes3
Has abstractyes

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