Who Reaches Out to Old Friends and What Do They Say?
Bibliographic record
Abstract
Social relationships, such as friendships, promote well-being. Yet research shows that most people have lost touch with a friend that they care about (i.e., have an “old friend”) and that they are reluctant to reach out after some time has passed. Given the emotional benefits of social connection and the potential that may come from reaching out to an old friend, we conducted three studies to examine what factors predict reaching out to an old friend. In Studies 1a-b, we coded a large corpus of previously collected reaching out notes (n = 863) along >20 textual dimensions to see which, if any, predicted whether the note was sent to an old friend. While a handful of dimensions were related to reaching out, none of these were consistent across studies, thereby providing limited insight into which text-based features predict reaching out behavior. In Study 2, we conducted a large survey (n = 312) to explore whether the author’s personality, trait happiness, or friendship beliefs were related to the likelihood of reaching out to an old friend. One exploratory dimension—friendship resiliency—predicted reaching out behavior, while friendship resiliency, openness to experience and friendship satisfaction predicted a willingness to reach out. Taken together, we found little evidence to suggest that characteristics of a reaching out note or the author predict whether the note is sent. We suggest that this failed search may mean that many people could be encouraged to reach out to their old friends under the right conditions.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".