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Record W4387145348 · doi:10.2298/soc2303356k

Are young people alone together: Experiences on the internet and attachment to offline and online friends?

2023· article· en· W4387145348 on OpenAlexaboutno aff
Dobrinka Kuzmanović, Oliver Tošković

Bibliographic record

VenueSociologija · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicImpact of Technology on Adolescents
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyThe InternetOperationalizationOnline and offlineOnline participationFeelingQuarter (Canadian coin)Social psychologyFriendshipSample (material)Computer-assisted web interviewingWorld Wide Web

Abstract

fetched live from OpenAlex

The aim of this study was to find out how young people perceive the quality of relationships with offline and online friends operationalized through anxiety and avoidance, and what the predictors of attachment to online friends are. Participants were 303 young people (78% girls), roughly equally divided between high school and university students (51% vs. 49%) aged 15 to 30 (Mage = 19.5, SD = 3.50). An online survey was conducted with a convenient sample. Two scales were used in the research: Experiences in Close Relationships and Excessive Internet Use, as well as questions about experiences on the Internet. The results show that youth are more likely to build and maintain close friendships through live interaction, despite their frequent use of social technologies. A quarter of respondents do not have a single friend with whom they mainly socialize online, while half have at most two online friends. Online friendships are more common among high school students. Most young people do not feel anxious in their relationships with offline and online friends; avoidance is significantly stronger in their relationships with online friends. The strongest predictors of attachment to online friends are attachment to offline friends and feeling personally safe online. When interpreting the obtained findings, one should bear in mind the limitations arising from the characteristics of the sample (it is not representative and gender-balanced).

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.217
Threshold uncertainty score0.362

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
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 head0.048
GPT teacher head0.358
Teacher spread0.311 · 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.

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

Citations1
Published2023
Admission routes1
Has abstractyes

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