The TEI Assignment in the Literature Classroom: Making a Lord Mayor’s Show in University and College Classrooms
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This article offers methods for implementing what Diane Jakacki and Katherine Faull identify as a digital humanities course at the assignment level, specifically one using TEI in college and university literature classrooms. The author provides an overview of his in-class activities and lesson plans, which range from traditional instruction to in-class laboratory exercises, in order to demonstrate an approach to teaching TEI that anticipates students’ anxieties and provides a gradual means of learning this new approach to literary texts. The article concludes by reflecting on how TEI in the classroom complicates critiques of the digital humanities’ proclivity to endorse neoliberal education models. By challenging simplistic renderings of the field and its tools, and by offering interconnections between TEI and traditional humanities practices, the author aims to supply a conscientious approach to designing TEI assignments to those interested but hesitant to include such assignments.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it