Bibliographic record
Abstract
Gamification has been a growing trend as a tool for pedagogical use. Incorporating game-design elements into a non-game context not only has the potential to amplify student motivation and interaction, but allows for the exploration of a new way to learn, teach, and understand history. Gregory of Tours’ Historia Francorum provides us with an interesting viewpoint into the world of 6th century Frankish Gaul. Understanding the motivations behind the characters in Gregory’s work and their relationship with the religious and political atmosphere of the 6th century can be difficult for modern readers. My project uses elements of role-playing to better engage students in interacting with the text. Students inhabit the role of a historical character from Historia Francorum and play out a narrative ‘campaign’ of multiple sessions as that character. Through research and primary source reading they will develop an understanding for their character, the world they inhabit, and their role within that society. Students engage with other students, prepare and give speeches, all in their prescribed roles. This, along with continual feedback on their actions given by the instructor, will cultivate a unique and more engaging learning atmosphere for students. My goal for this undergraduate research project is to develop an alternative approach to understanding the dynamics of early medieval Frankish society. By turning Gregory’s work into an inhabitable space, the large body of text becomes approachable and engaging to students. The final phase of the project which is underway, is testing it in a third year undergraduate history course.
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 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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".