Challenging new views on familiar plotlines: A discussion of the use of XML in the development of a scholarly tool for literary pedagogy
Bibliographic record
Abstract
This article describes PlotVisML, a simple, flexible XML schema for encoding literary narratives that was developed by an interdisciplinary team of researchers in literary studies, interface design, computing studies, and education as part of a research project on reading, writing, and teaching complex literary narrative. PlotVisML is a simple, adaptable schema consisting of five key elements: , , and (tags for marking up narrative events), and and (tags for encoding narrative objects). Fictional narratives that have been marked up using PlotVisML can be visualized in PlotVis, a digital scholarly tool that allows users to model and interact with literary narratives in three dimensions. Both PlotVis, an interactive visualization tool, and PlotVisML, our custom XML schema for encoding literary narratives, were designed to permit challenging new views on familiar plotlines and, more importantly, to depart from conventional ways of modeling narrative in literary instruction. In discussing the process of developing PlotVisML, we contribute to the ongoing discussion of text encoding as a form of close reading (e.g., Liepert, 2009).
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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 | 0.042 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.009 | 0.039 |
| Scholarly communication | 0.020 | 0.049 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 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".