The use of Facebook in romantic relationships: An actor-partner interdependence mediation model predicting relationship visibility
Bibliographic record
Abstract
The purpose of this paper was to document the use of social media in romantic relationships. More specifically, we examined whether the information that people desired to share (i.e., desired relationship visibility) and shared in practice (i.e., actual relationship visibility) about their romantic relationships on Facebook was predicted by their level of relational commitment. A sample of 139 couples, users of Facebook, aged 17 to 30 years, participated in the study. Participants completed questionnaires and used the Friendship application on Facebook (which gathered data directly from their Facebook accounts). The mediating role of desired relationship visibility in the link between relational commitment and actual relationship visibility on Facebook (i.e., declared relationship status and transient relationship visibility) was investigated using path analyses for dyadic data. Results of actor-partner interdependence mediation model analyses confirmed that women’s relational commitment was positively associated with their desired relationship visibility on Facebook. Men’s and women’s desired relationship visibility were, in turn, associated with their own and their partner’s declared relationship status and their own transient relationship visibility on Facebook. Our results provided evidence of the dyadic nature of Facebook self-presentations of coupledom.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".