Bibliographic record
Abstract
In this article, I study two tropes with a long-standing tradition in Latin American fiction: museums and machines. Specifically, I analyze Galo Ghigliotto’s 2019 novel El museo de la bruma from a two-fold perspective. First, as an example of a larger corpus of contemporary fiction that has taken to material culture as a refuge for storytelling in a world where the digital increasingly mediates the human experience. Second, in relation to a reference made in the book to Adolfo Bioy Casares’s 1940 fictional character Morel as the main architect of the museum. Some of the questions I explore in this article are: how does the relationship between ekphrasis and collective memory work? How does the body regulate the framing of memories regarding the catalog depicted in the novel? If, in Bioy Casares’s novel, the machine is the trope that destabilizes reality by duplicating a body, what happens with the incorporation of this fiction eighty years later in Ghigliotto’s novel when the digital realm has become (or threatens to become) reality? How is contemporary fiction responding to this change?
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.047 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".