An empirical, accessible definition of “ghosting” as a relationship dissolution method
Bibliographic record
Abstract
Abstract “Ghosting” as a method of relationship dissolution has entered both popular media and academic discussion as a topic of interest. Journalists and researchers have used both observation and qualitative methods to define this breakup strategy with varying and sometimes contradictory results. The goal of this study was to create an accessible and empirical definition of ghosting and to resolve discrepancies between existing definitions. To do so, we asked 499 participants (321 cisgender women, all residing in Canada and aged 17–29) two open‐ended questions about ghosting. Participants provided their own definition of ghosting, and then identified behaviors that they associated with ghosting. Next, we conducted inductive qualitative analyses with four cycles of coding to determine the key components of the behavior that distinguish ghosting from other methods of relationship dissolution. Based on participant responses and language, we derived the following definition of ghosting: “One way that people can end a relationship is by ghosting. Ghosting is when one person suddenly ignores or stops communicating with another person, without telling them why.” Our proposed definition of ghosting addresses shortcomings presented by previous and concurrently developed definitions and provides a starting point for future research on ghosting in romantic relationships, friendships, workplaces, and beyond.
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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 | 0.062 | 0.148 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.007 | 0.027 |
| Scholarly communication | 0.007 | 0.013 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".