Fra bakteriernes perspektiv. Positioner i forholdet mellem litteratur og naturvidenskab belyst gennem Mark Twains “3,000 Years Among the Microbes” og Christian Böks “The Xenotext Experiment”
Bibliographic record
Abstract
Jens Lohfert Jørgensen: “From the Perspective of Bacteria. Position in the Relationship Between Literature and Science Illustrated by Mark Twain’s 3,000 Years Among the Microbes and Christian Bök’s The Xenotext Experiment”This article discusses how literature administers scientific notions about bacteria based on two curious examples: Mark Twain’s uncompleted novel 3,000 Years Among the Microbes (1905) and the Canadian experimental poet Christian Bök’s work The Xenotext Experiment (2007-). There are some noteworthy correspondences between the works’ engagement with bacteria: They both attempt to establish a microbial perspective (Twain by using a cholera bacterium as narrator, Bök by trying to make a bacterium produce a poem), they both use this engagement to experiment with literary form, and they are both closely related to the natural sciences. On the backdrop of these correspondences, the article sketches out a typology of five modal positions in the relationship between literature and science: Mediating, satirical, allegorical, ontological, ludic. The typology has a local character, but can form a starting point of the discussion of further modal positions.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.068 | 0.044 |
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".