Ghosting: Abandonment in the Digital Era
Bibliographic record
Abstract
This entry synthesizes the multidisciplinary literature on ghosting published through late 2023 across psychological and social science journals. Search terms include “ghosting” and “online dating”. Both quantitative and qualitative studies are included. The rise in ghosting can be attributed to advancements in technology and the increased popularity of dating apps. It is defined as an abrupt one-sided ending, without explanation, of an established friendship/romantic or other communication connection. The prevalence of ghosting has increased, as reported by both ghosters (i.e., persons who stopped responding) and ghostees (i.e., persons who were “dumped”). Individuals characterized by dark triad traits (i.e., psychopathy, Machiavellianism, and narcissism) are more likely than others to be ghosters. These individuals have a history of using ghosting as their preferred method of ending relationships without concern for its negative impact on ghostees or, indeed, on themselves. The psychological effects of ghosting can influence mental health, although most individuals ultimately find ways of coping.
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 | 0.003 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 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".