MétaCan
Menu
Back to cohort
Record W4224988949 · doi:10.31234/osf.io/6grz7

Creativity and Smartphone Use: Three Correlational Studies

2022· preprint· en· W4224988949 on OpenAlexafffund
Jay A. Olson, Dasha A. Sandra, Ellen J. Langer, Amir Raz, Samuel P. L. Veissière

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicImpact of Technology on Adolescents
Canadian institutionsMcGill University
FundersFonds de Recherche du Québec - SantéCanada First Research Excellence FundMcGill University
KeywordsCreativityPsychologyDivergent thinkingCreative thinkingExploratory researchSample (material)Smartphone applicationSocial psychologyApplied psychologyMultimediaSocial scienceComputer scienceSociology

Abstract

fetched live from OpenAlex

Various books and popular media outlets claim that mindless smartphone use can impair creativity, yet few studies have tested this idea. We conducted a survey and three correlational studies focused on divergent thinking, the mindful ability to generate creative responses to open-ended problems. Most of the 48,000 participants surveyed thought that smartphone use reduced creativity. This view was consistent with the negative correlations we found between divergent thinking and both screen time and problematic smartphone use (rs = −.27 to −.35) in an exploratory sample of 62 university students. However, in two pre-registered replications with larger and more diverse samples (N = 294 and 16,932), we found at most tiny correlations between measures of divergent thinking or creative achievement and several types of smartphone use (rs = −.09 to .09). Thus, the link between smartphone use and creativity may be weaker or more nuanced than is commonly believed.

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)0.009
metaresearch head score (Gemma)0.026
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.026
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.004
Science and technology studies0.0020.002
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.116
GPT teacher head0.396
Teacher spread0.280 · 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 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

Citations6
Published2022
Admission routes2
Has abstractyes

Explore more

Same topicImpact of Technology on AdolescentsFrench-language works237,207