MétaCan
Menu
← Back to cohort
Record W4243778895 · doi:10.32920/ryerson.14648913

Are media exposure and self-control related?

2021· preprint· en· W4243778895 on OpenAlexaff
Julian House

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldPsychology
TopicBehavioral Health and Interventions
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsTraitSelf-controlPsychologyControl (management)Reading (process)Dimension (graph theory)Social psychologyPerceived controlComputer scienceMathematicsArtificial intelligence

Abstract

fetched live from OpenAlex

One critical dimension of self-control is attention control, the ability to willfully determine the content of conscious thought. It is argued here that the amount of effort required to exercise attention control while critically engaging in different media, specifically text and television, is significantly different. It is hypothesized that the amount of self-control exerted while reading will be significantly greater than while watching television. An experiment comparing a film clip with its screen play demonstrates that participants' self-control is more depleted after 30 minutes of reading than 30 minutes of viewing. Furthermore, it is hypothesized that differential habitual exposure to media will predict trait levels of self-control, respectively. An internet survey testing these relationships is reported in which a small but significant negative relationship between TV exposure and trait self-control is found.

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)0.001
metaresearch head score (Gemma)0.013
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 score0.010
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0000.002
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0100.000

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 head0.047
GPT teacher head0.363
Teacher spread0.316 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

Explore more

Same topicBehavioral Health and Interventions→French-language works237,207→