To Mourn, To Re-imagine Without Oneself: Death, Dying, and Social Media/tion
Bibliographic record
Abstract
This paper incorporates and reflects on Steinberg’s particular vantage as a dying person whose blog engages the transforming ecologies of mourning and the place(s) of dying in the emergent spaces of social media. The paper homes in on the distinction between the repudiation of death and the repudiation of mourning in the collective project of “re-imagining without oneself,” that is, of re-imagining another life, another death, beyond the liberal coordinates of a “you” and a “me.” As an “intermediating” place, we argue that the blog serves as a virtual portal that both problematizes and (re)mediates the personal and the political. In so doing, the paper touches on key feminist political questions concerning bodily self-sovereignty; the broader racialized, classed, and gendered cultural imaginary; and the place of mourning in the analogy of the personal body in crisis with the myriad crises of the body politic at this significant and difficult cultural moment. Particularly, with the outcomes of Brexit and the Trump victory in the American election, this is a time of loss as the complex consensus of liberal democracy has broken down and neoliberal body-affective practices morph into isolationist nationalisms and the resurgence of movements against social justice and equality.
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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.067 |
| Scholarly communication | 0.017 | 0.012 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".