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Record W4409768312 · doi:10.54097/jk420h93

The Association Between Social Media and Self-Esteem among Adolescents

2025· article· en· W4409768312 on OpenAlexaff
Leran Wang

Bibliographic record

VenueJournal of Education Humanities and Social Sciences · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicImpact of Technology on Adolescents
Canadian institutionsOntario College of Art and Design
Fundersnot available
KeywordsAssociation (psychology)Self-esteemPsychologySocial mediaDevelopmental psychologySocial psychologyClinical psychologyComputer sciencePsychotherapistWorld Wide Web

Abstract

fetched live from OpenAlex

This article examines how social media impacts adolescents' self-esteem. Critical factors such as social comparison, feedback from others, and introspection play a key role in shaping users' self-esteem during their time on these digital platforms. These factors include social comparison, feedback from others, and introspection, all of which greatly affect the self-esteem of users as they engage with diverse social media settings. Additionally, the article considers how cultural variations affect the link between social media and self-esteem. It points out that people from Eastern cultures, which tend to stress collectivism and social harmony, might be more vulnerable to the impacts of social media interactions on their self-esteem. The article offers a thorough examination of the complex relationship between social media and teenagers' self-esteem, taking into account various psychological factors and cultural differences. In doing so, it emphasizes the importance of self-esteem as a predictor of life satisfaction and highlights the necessity for more research in this field to better comprehend and tackle the challenges that teenagers encounter in the digital era.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.067
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0060.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.033
GPT teacher head0.335
Teacher spread0.303 · 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 designObservational
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
Published2025
Admission routes1
Has abstractyes

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