Re-enacting painful and sensitive memories on social platforms through fictional profiles
Bibliographic record
Abstract
The possibility of passing for someone else did not emerge with social media. However, the fact that, as the saying goes, “on the Internet, nobody knows you’re a dog,” has made building fictitious identities much easier. In this paper, we will explore the hypothesis that beyond trivial experiments with pseudonyms, the option of experimenting with “versions of oneself” on social platforms has given rise to a new genre, that we term “fictional profiles.” We will consider the fictional profile as a symbiotic agent, pointer and witness to contemporary society. After a general introduction to the genre and a critical discussion of methodologies to identify its specificities, we will focus on two re-enactments of historical events and figures on Facebook and Instagram. We will discuss the problematic nature of these works in terms of valorization, preservation and archiving insofar as, on the one hand, they question the classical categories of the work, the author and the reader; and, on the other hand, they are fundamentally dependent on their publication platforms.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.014 |
| Scholarly communication | 0.009 | 0.015 |
| Open science | 0.001 | 0.007 |
| 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".