motif de la militia amoris dans les élégies de Properce et Les amours I.9 d’Ovide: une analyse schématique
Bibliographic record
Abstract
The aim of this article is to conduct a schematic analysis of the principal topoi associated with the motif of militia amoris, evaluating their incorporation by Propertius and Ovid within their respective elegies. Additionally, the article seeks to scrutinize the differentiations between these two poets regarding the thematic aspect of militia amoris. To address these inquiries, the study employs a comparative analysis, drawing parallels specifically between Ovid’s Amores, I, 9, and selected excerpts from Propertius’ elegies that engage with this motif. The article’s progression is structured around two distinct phases: initially, it will be necessary to establish a comprehensive definition of militia amoris and outline its fundamental characteristics. Subsequently, an in-depth analysis will ensue, scrutinizing how Propertius and Ovid handle this theme. This methodological framework is devised to facilitate not only an assessment of the idiosyncrasies inherent to each poet but also an exploration of the points of convergence and divergence between them.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".