Towards a linked open data resource for direct speech acts in Greek and Latin epic
Bibliographic record
Abstract
Abstract The Digital Initiative for Classics: Epic Speeches (DICES) research group reports here on preliminary work to integrate research on Greek and Latin epic speeches into the larger ecosystem of linked open data (LOD) for classical scholarship. The ability to collate speech data from different researchers and to leverage external repositories of texts and characters opens up new possibilities for interrogation of the epic corpus. We briefly survey the current state of scholarship on epic speeches and of the digital infrastructure on which we propose to build. We outline a model for harmonizing speech data across studies and aligning it with existing LOD standards. Finally, we discuss some early proof-of-concept results and the larger implications of this approach for the field. The long-term aim of the DICES project is to build a database of metadata on direct speech in Greek and Latin epic, not only covering canonical texts such as Homer and Virgil, but also including the less-studied texts of the late antique period, which will benefit greatly both from the increased accessibility and also from the diachronic perspective afforded by a corpus-based approach. The envisioned database also has the potential to include diachronic data from additional genres and languages at a later stage.
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 | 0.026 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.017 | 0.013 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.010 | 0.015 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.006 |
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".