Bibliographic record
Abstract
Personal networks undergo change in response to major life course events. Individual, relational, and network characteristics that influence network instability in the absence of a significant life transition/crisis are less understood. We focus on those ties that transition from active to dormant. Because the shift to dormancy is often interpreted as a reduction in support or social capital, it is considered problematic. This study is based on longitudinal survey data of middle‐class adults who did not undergo life changes. Even in this context of relative stability, support networks experience rates of dormancy similar to those observed during periods of major upheaval. Tie dormancy is unrelated to individual characteristics, network size and density, or homophily along dimensions other than sex. Frequency and medium of communication are particularly notable as factors that were not related to tie dormancy. Ties were less likely to become dormant if they were geographically or emotionally close, immediate kin or neighbors, highly supportive, the same sex, or more embedded in the network. These findings provide context for how support networks operate when not buffeted by exogenous forces. They provide a baseline for understanding the impact on networks of transitions, trauma, new media, and difficult life circumstances.
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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".