The Slipperiness of Literary Maps: Critical Cartography and Literary Cartography
Bibliographic record
Abstract
How we read and interpret a map when it is presented alongside the text in a work of fiction is the central issue with which this paper is concerned. Although “literary maps” can be found across a range of genres in literary studies, they are often treated as illustrative rather than being understood as integral to the meaning of the literary work. This article seeks to challenge such assumptions. The first half of the article is interdisciplinary, engaging with the work of J.B. Harley, Mark Monmonier, Franco Moretti, Christina Ljungberg, and Andrew Thacker in order to open up responses to literary maps in more complex ways. It draws on critical cartography to define core concerns for an emerging literary cartography, such as the nature of the analogy between map and text; the complexity of correspondence when a map and text occur alongside each other and the author is also the map-maker; and the difficulties created by naïve users of the literary map. The second half of the article grounds the prior discussion in analysis of Agatha Christie's house plans in The Mysterious Affair at Styles and The Murder of Roger Ackroyd.
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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 | 0.020 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.011 | 0.008 |
| Science and technology studies | 0.020 | 0.106 |
| Scholarly communication | 0.030 | 0.026 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".