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Record W3154269307

"Technoference" and Implications for Mothers' and Fathers' Couple and Coparenting Relationship Quality

2018· article· en· W3154269307 on OpenAlexaff
Brandon T. McDaniel, Adam M. Galovan, Jaclyn D. Cravens, Michelle Drouin

Bibliographic record

VenuePMC · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicWork-Family Balance Challenges
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsCoparentingPsychologyQuality (philosophy)Social psychologyPerceptionDevelopmental psychologyInterpersonal relationship
DOInot available

Abstract

fetched live from OpenAlex

Technology devices are widely used today, creating opportunities to connect and communicate with distant others while also potentially disrupting communication and interactions between those who are physically present (i.e., technoference or phubbing). These disruptions in couple and coparenting relationships have the potential to negatively impact relationship outcomes. In this two-part study of 182 married/cohabiting couples from the Daily Family Life Project and 239 couples from the Couple Well-Being Project, we examined the role of technoference in couple and coparenting relationship quality and potential gender differences utilizing dyadic data. We found that greater technoference related to greater conflict over technology use, and greater conflict predicted lower relationship satisfaction and poorer perceptions of coparenting quality (Study 1). Using a more diverse sample (Study 2), we again found support for the main pathways tested in our first study, suggesting that results found in Study 1 and in previous work are not artifacts of sampling. As satisfaction, support, and agreement among relationship partners and parents are often critical to relationship health and family cohesion, it is important for couples and families to evaluate, monitor, and be willing to adapt their technology usage patterns so that these patterns do not cause conflict and possibly relationship deterioration over time.

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)0.000
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 score0.008
Threshold uncertainty score0.466

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.099
GPT teacher head0.388
Teacher spread0.289 · 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

Citations0
Published2018
Admission routes1
Has abstractyes

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