A Divine Inspiration for Border Studies; Conceptually Excavating the Polydivine Roman Border Landscape of Terminus, Janus, Mercurius, Trivia and Pluto
Bibliographic record
Abstract
What can we learn from the Romans regarding the understanding of borders? For various contemporary populist politicians, Roman history teaches us the need for harsh and strict borders, to prevent the invasion of “barbarians” and the “fall” of the European Union. To assess their claim, we trace back the Romans’ own source of inspiration for their territoriality and border ideology: the Roman border gods. Using this conceptual archaeological lens we critically counter the populist reductionism and selective history shopping, and explore the potential of the diverse Roman gods to further enrich the contemporary border studies academic debate. We conceptually excavate the polydivine dialogue among significant Roman border-related gods: Terminus, whose representation of border fixity inspires the dominant politicized debate on ultra-securitised borders, along with Janus, Mercury, Trivia, and Pluto, each offering diverse perspectives on borders. Diving into the divine inspiration of the Roman border gods, it becomes evident that we can learn a lot from the Romans indeed – and far more than the misleading and selective interpretations presented by populist politicians.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.046 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".