Reclaiming the Classics for a Diverse and Global World Through OER
Bibliographic record
Abstract
In the 2019–20 academic year, I redesigned a course on the classics to make both the texts and the context in which they were taught more accessible for and relevant to the predominantly female students of Saint Mary’s College, Notre Dame. The course was re-centered on the dialogue between the ever-evolving and diverse cultures within Greece and the Roman empire and surrounding regions such as Egypt, Ethiopia, and Persia; issues caused by slavery and economic inequality; conceptions of gender roles and sexuality, race and ethnicity, and migration and citizenship; the troubling appropriation of classical motifs and texts by fascist groups in the twentieth century and some alt-right groups and sexual predators in the twenty-first century; and on recent initiatives meant to demonstrate the diversity of both Greek and Roman cultures through documentary, artistic, and archaeological evidence (particularly in the digital humanities and in museums and libraries). I also wanted to make the course close to zero cost for students and to shift to digital texts which lent themselves to interactivity and social scholarship. Our librarian, Catherine Pellegrino, obtained multi-user e-books for modern reinterpretations of classical works still in copyright. A LibreTexts grant enabled the co-authors of this article—the course instructor (and lead author) and two paid student researchers—and a team of summer-employed student collaborators to edit, footnote, and create critical introductions and student activities for various key texts for the course. Many of these texts are now hosted on the LibreTexts OER platform. Beta versions of enriched OER texts and activities were user tested in a synchronous hybrid virtual/physical classroom of twenty-five students, who were taking the course (HUST 292) in the fall semester of 2020.
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.
How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".