Co‐creating real fictional characters: Virtual ethnofabulation
Bibliographic record
Abstract
Abstract We generated these short fiction stories from a collaborative co‐writing practice. In this practice, we write ethnofiction together, alternating who is writing. When we write, we write live in a Google Doc while also on a video call; one of us lives in Sudbury, Ontario, and the other lives in Toronto. In our practice, we are interested in exploring the potential of collective world‐building as critical empirical and relational praxis. The three pieces included here are character studies, but they are also stories of characters studying. Each of them arises from an oblique or direct relation to our ethnographic fieldwork, one of us having worked with online queer and trans activists in North America, and the other having worked with working‐class community organizers in Barcelona. The first piece considers the role of representation in mediating violence and the emotional development of the observer. The second deals with the ways in which loss, abandonment, and inheritance are experienced through intimate attachments to place and to flesh. The final story was written as an exercise in listening: the one of us who works in Barcelona described an interlocutor to the one of us who works in North America. We then wrote a story about a character called “Arnoldo,” who is neither wholly fictional nor wholly real. This work develops a set of techniques for relating to others at the intersection of artistic craft, critical fabulation, and world‐building.
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.017 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| 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".