Bibliographic record
Abstract
Ghosting, or avoiding technologically-mediated contact with a partner instead of providing an explanation for a breakup, has emerged as a relatively new breakup strategy in modern romantic relationships. The current study investigated differences in the process of relationship dissolution and post-breakup outcomes as a function of breakup role (disengager or recipient) and breakup strategy (ghosting or direct conversation) using a cross-validation design. A large sample of participants who recently experienced a breakup was collected and randomly split into two halves. Exploratory analyses were conducted in Sample A and used to inform the construction of specific hypotheses which were pre-registered and tested in Sample B. Analyses indicated relationships that ended through ghosting were shorter and characterized by lower commitment than relationships that ended directly. Recipients experienced greater distress and negative affect than disengagers, and ghosting disengagers reported less distress than direct disengagers. Ghosting breakups were characterized by greater use of avoidance/withdrawal and distant/mediated communication breakup tactics and less open confrontation and positive tone/self-blame breakup tactics. Distinct differences between ghosting and direct strategies suggest developments in technology have influenced traditional processes of relationship dissolution. Cover photo by Kelly Sikkema on Unsplash.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.025 | 0.006 |
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".