Why Map Literature? Geospatial Prototyping for Literary Studies and Digital Humanities
Bibliographic record
Abstract
By focusing on the process of building A Map of Paradise Lost—a geospatial humanities text-to-map project that visualizes the locatable places in John Milton’s Paradise Lost— this paper addresses the question “why map literature?” and demonstrates how the process of research prototyping is in itself a form of knowledge production. Through a series of prototyping moments, we address how the different steps involved in building a geospatial humanities project can produce new knowledge about the fields it relates to: literary studies and digital humanities. The prototyping moments make arguments that advance our understanding of Milton’s Paradise Lost, approaches to data visualization for cartographic comparison in and beyond DH, and models for interdisciplinary collaboration. Résumé En se concentrant sur le processus de construction d’une Carte du Paradis Perdu – un projet d’humanités géospatiales qui visualise, dans une carte à partir du texte, les places localisables dans le Paradis Perdu de John Milton – cet article aborde la question «pourquoi une littérature cartographique?» et démontre comment le processus de prototypage de recherche est en soi une forme de production de connaissances. A travers une série de moments de prototypage, nous abordons comment les différentes étapes impliquées dans la construction d’un projet d’humanités géospatiales peuvent produire de nouvelles connaissances concernant les domaines relatifs à ce sujet: études littéraires et humanités digitales. Les moments de prototypage présentent des arguments qui améliorent notre compréhension du Paradis Perdu de Milton, des approches de visualisation des données pour une comparaison cartographique «à l’intérieur» et «au-delà» des humanités digitales ainsi que des modèles pour une collaboration interdisciplinaire. Mots-clés: cartographie littéraire; prototypage géospatial; communication savante; Paradis Perdu
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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 | 0.018 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.009 | 0.028 |
| Scholarly communication | 0.018 | 0.019 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".