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Record W2778529358 · doi:10.25071/ryr.v3i0.40409

The Downside of the Web: Compulsive Internet Use, Empathy, and Altruism

2016· article· en· W2778529358 on OpenAlexaboutno aff
Sergey Mazuritsky

Bibliographic record

VenueRevue YOUR Review (York Online Undergraduate Research) · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicImpact of Technology on Adolescents
Canadian institutionsnot available
Fundersnot available
KeywordsEmpathyAltruism (biology)PsychologyThe InternetSocial psychologyScale (ratio)CorrelationDevelopmental psychology

Abstract

fetched live from OpenAlex

Reliance on the internet can become problematic, and can therefore be related to deficits in pro-social behaviours and the associated cognitive and emotional processes. This research examined the relationship between compulsive internet use, empathy, and altruistic behaviours. It was hypothesized that a positive correlation would exist between measures of empathy and altruism, and a negative correlation between compulsive internet use and both of the measures of pro-social behaviour. A survey study was conducted that compared scores on the Compulsive Internet Use Scale, The Toronto Empathy Questionnaire, and the Self-Report Altruism Scale. Participants were 161 York University students with a mean age of 22; 65% were female. Results support the three hypotheses, indicating the existence of a significant correlation, as predicted. Implications regarding workplace practices and quality of life are discussed.

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)0.002
metaresearch head score (Gemma)0.008
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.002
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.108
GPT teacher head0.399
Teacher spread0.291 · 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
Published2016
Admission routes1
Has abstractyes

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