How Friendship Network Characteristics Influence Subjective Well-Being
Bibliographic record
Abstract
Abstract This article explores how friendship network characteristics influence sub-jective well-being (SWB). Using data from the 2003 General Social Survey of Canada, three components of the friendship network are differentiated: number of friends, fre-quency of contact, and heterogeneity of friends. We argue that these characteristics shape SWB through the benefits they bring. Benefits considered are more social trust, less stress, better health, and more social support. Results confirm that higher frequency of contacts and higher number of friends, as well as lower heterogeneity of the friendship network are related to more social trust, less stress, and a better health. Frequency of contact and number of friends, as well as more heterogeneity of the friendship network increase the chance of receiving help from friends. With the exception of receiving help from friends, these benefits are in turn related to higher levels of SWB. Only the frequency of meeting friends face-to-face has a remaining positive direct influence on SWB.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".