MétaCan
Menu
Back to cohort
Record W3120496178 · doi:10.29173/bsuj492

Mere Presence of a Cell Phone: Effects on Academic Ability

2020· article· en· W3120496178 on OpenAlexaffvenue
Vanessa C. Boila, Tru E. Kwong, Jaimey E. Hintz

Bibliographic record

VenueBehavioural Sciences Undergraduate Journal · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicImpact of Technology on Adolescents
Canadian institutionsMount Royal UniversityUniversity of Alberta
Fundersnot available
KeywordsPhoneSpellingPsychologySentenceComprehensionAffect (linguistics)Social psychologyCommunicationComputer scienceLinguisticsNatural language processing

Abstract

fetched live from OpenAlex

Prior research has suggested that cell phone use in the classroom and during learning-related tasks is detrimental to academic performance. Recently, the mere presence of a cell phone has been found to negatively affect relationships and to impair performance on learning and cognitive tasks. This study explored whether the presence (visibility without use) of a cell phone negatively impacts one’s performance on tests measuring preexisting academic ability. The study evaluated 45 participants; some were enrolled in an introductory psychology course, and others were members of the public. Three subtests from the Wide Range Achievement Test (WRAT-4) were completed: spelling, sentence comprehension, and mathematics. During testing, half of the participants had cell phones, and the other half did not. Statistical analyses revealed no significant difference between the cell phone-present and cell phone-absent group on the sentence comprehension (p=.52), spelling (p=.07), and mathematics subtest (p=.11). Unexpectedly, a non-significant trend was observed in the opposite direction; that is, the cell phone-present group outperformed the cell phone-absent group on all subtests. Therefore, the original hypothesis suggesting that the cell phone-present group would be significantly poorer at demonstrating preexisting skills on tests of academic ability in comparison to the cell phone-present group was not supported.

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 score0.270
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.002
Scholarly communication0.0000.001
Open science0.0020.000
Research integrity0.0000.001
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 head0.056
GPT teacher head0.342
Teacher spread0.286 · 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

Citations6
Published2020
Admission routes2
Has abstractyes

Explore more

Same venueBehavioural Sciences Undergraduate JournalSame topicImpact of Technology on AdolescentsFrench-language works237,207