Examining the Qualities of Online and Offline Friendships: A Comparison Between Groups
Bibliographic record
Abstract
Online social technologies are now used by a majority of individuals in the U.S. (Pew, 2018a). Sending emails, texting, posting on social media sites, and connecting with others through online gaming open up our social networks to a wider range of individuals. As a result, it is not uncommon to develop friendships with others that are conducted primarily in an online environment. However, we know little about the qualities of online friendships and how they may, or may not, differ from traditional face to face friendships. The present study focused on exploring friendship quality in online and offline domains using two different groups: a gamer group and a non-gamer group that used non-gaming applications to connect with others online. All participants completed a demographic questionnaire to gather information about their online and face to face friendships, the McGill Friendship Questionnaire (Mendelsohn and Aboud, 2014) for their closest online and offline friends and measures of happiness, anxiety, and depression. In Study 1, within group comparison found that gamers’ online friendships were of significantly higher quality than their offline friendships. For non-gamers, the opposite results were found. A second study was done using a larger, non-college-based sample. Results of Study 2 found that for gamers and non-gamers offline friendships were of higher quality than online friendships, although both types of friendships existed in both groups. There were no differences between groups in general life happiness, anxiety or depression. Suggestions for follow-up research are presented.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".