Becoming Spectral: Toward a Media History of Ghosting
Bibliographic record
Abstract
This article contextualizes contemporary forms of digital ghosting by examining how two of its historical precursors—Victorian calling culture and answering machines—have been represented in North American women’s magazines. To do so, we develop mediated avoidance as an analytical heuristic. This concept captures the material, relational and social dimensions of a set of understudied media practices that seek to strategically engage with the gaps that are inherent in all communication, to defer, deflect, or disrupt mediated connections. Representations of mediated avoidance from respective eras were found to reflect different anxieties over the management of the public/private divide. Calling culture relied on unpaid labor to facilitate the transmission of printed messages between bourgeoise women and was constrained by an array of social protocols that regulated interactions along conceptions of propriety. The disconnective features of answering machines, meanwhile, were represented as giving women the upper hand in courtship, as well as providing means for increased productivity and self-care, foreshadowing contemporary justifications of digital disconnection. Concerns over contemporary ghosting are discussed as produced by a spillage of media practices. Ghosting is considered acceptable in feminine-coded spheres like courtship. But it is viewed as inappropriate—sometimes even as signaling a broader social crisis—when it bleeds into other contexts, like when an employee ghosts their employer.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.014 | 0.039 |
| Scholarly communication | 0.013 | 0.018 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".