More Information than You Ever Wanted: Does Facebook Bring Out the Green-Eyed Monster of Jealousy?
Bibliographic record
Abstract
The social network site Facebook is a rapidly expanding phenomenon that is changing the nature of social relationships. Anecdotal evidence, including information described in the popular media, suggests that Facebook may be responsible for creating jealousy and suspicion in romantic relationships. The objectives of the present study were to explore the role of Facebook in the experience of jealousy and to determine if increased Facebook exposure predicts jealousy above and beyond personal and relationship factors. Three hundred eight undergraduate students completed an online survey that assessed demographic and personality factors and explored respondents' Facebook use. A hierarchical multiple regression analysis, controlling for individual, personality, and relationship factors, revealed that increased Facebook use significantly predicts Facebook-related jealousy. We argue that this effect may be the result of a feedback loop whereby using Facebook exposes people to often ambiguous information about their partner that they may not otherwise have access to and that this new information incites further Facebook use. Our study provides evidence of Facebook's unique contributions to the experience of jealousy in romantic relationships.
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 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 | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".