Developing Digital Mappaemundi: An Agile Mode for Annotating Medieval Maps
Bibliographic record
Abstract
Digital Mappaemundi (DM) is a resource under development to create open source tools for scholars to edit and annotate image and textual data content as linked data, and for other users to search within this rich content. For the purposes of development, our data have been medieval mappaemundi ("maps of the world") and transcriptions of their geographical source texts.The second phase of DM's alpha development (2009-10) allows users to work with digital images of maps from medieval manuscripts, mark regions-of-interest within images, and associate textual annotations with those regions and then link one or more sets of digital texts to these regions, or target one or more words within these texts as targets to these regions. Scholars may create markers images with individual points, segmented lines, or custom polygonal shapes. Significantly, a scholar may identify any number of markers on any number of images as the targets for textual annotation and link them to any number of digital texts or locations within these texts. Additionally, a given marker may serve as the target for any number of textual annotations. Scholars may organize their annotations into groups called layers so that different research questions involving a single image may be addressed separately through annotation. Scholars may choose to view a single layer of annotation or view multiple layers of annotation overlaid on one another. A robust search function also allows users to organize the annotated content dynamically. At the time of this publication DM has undergone significant evolution in its phase three beta development, with applications for annotation and linked data beyond the original use case of medieval maps. For current functionality and features of the DM environment, as well as a list of medievalist projects using it, see http://ada.drew.edu/dmproject/.
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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 | 0.014 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.013 |
| Open science | 0.004 | 0.017 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.027 | 0.021 |
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".