Internet Relationships and Their Impact on Primary Relationships
Bibliographic record
Abstract
Abstract The number of personal relationships occurring via the Internet is increasing as more people gain access to it. Many of these relationships are romantic in nature, and evidence is accumulating that they have the potential to have an adverse effect on existing face-to-face relationships. This study explored the formation of romantic relationships on their Internet, their nature, and their possible impact on existing marital or de facto relationships in a sample of 75 adults (mean age 42 years, SD = 11.1 years) who responded to an online survey of individuals involved in extradyadic relationships on the Internet. Respondents reported a variety of means of contacting their online partner. More females than males communicated with them daily. Most respondents knew what their partner looked like, most had contacted them by telephone, and a third had met them. Most reported more satisfaction with their online relationship than with their face-to-face one, though few said that it was more important to them than their primary relationship. Although only a quarter of the sample admitted that their online relationship had affected their primary one, those participants reported concealing the truth about the time or nature of their activities, that everyday tasks did not get done, and that levels of sexual intimacy with their primary partner had dropped. The nature of these and other problems suggests that therapists should be aware of the potential for Internet relationships to seriously affect face-to-face relationships.
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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 | 0.001 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.001 |
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".