Writing from the rubble: Digital epistolary friendship (post)apocalypse
Bibliographic record
Abstract
This paper interweaves fragments of our digital epistolary exchanges with the exercises and prompts we practiced during long-distance online meetings. We reflect on our first conversation, sparked in a taxi en route to an abandoned construction site in Oaxaca—once meant to be a luxury hotel, now reclaimed by a local arts collective. Amidst its post-apocalyptic remains, we found ourselves fervently discussing class hierarchies in the UK, from supermarket rankings to the neoliberalisation of higher education. Our shared frustrations as feminist scholars navigating colonial academia led us to seek alternative ways of thinking, writing, and creating. Inspired by our engagement with La Pocha Nostra, Vanessa de Machado de Oliveira’s Hospicing Modernity, and the Gesturing Toward Decolonial Futures collective, we explore online performance practices that disrupt the linear, univocal narratives reinforced by hetero-patriarchal, colonial, and capitalist citational traditions. Through this work, we intentionally center friendship and joy as regenerative praxis—while resisting their reduction to depoliticised individualism. Holding space for contradiction, complicities, and complexity, we draw on exercises from Machado de Oliveira and La Pocha Nostra to structure our creative methodologies, embracing messiness as a necessary condition for epistemic and artistic transformation.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.024 | 0.003 |
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".