Command and Consilium: On Infrastructures of Decision-Making in Roman Culture
Bibliographic record
Abstract
The aim of the following considerations is to demonstrate why decision-making processes can profitably be understood as infrastructures of political space.This is not self-evident, and we should not, without distinction, label every formation of political opinion an infrastructure.However, what forms decision-making processes according to culturally modelled understandings, by virtue of their defining the decision-making spaces, modalities, and participants, might be called infrastructure, indeed.Those decision-making processes are not due to chance, but manifestations of the regular and repeatable within the framework of a specific political culture.Ancient Rome is a particularly rewarding test-case because Roman decision-making processes appear to have been characterised by contradictory principles.In the following, I will argue that this supposed paradox was in fact none, as those contradictory principles were mediated by the interplay of visibility and concealment.Visibility therefore emerges as a category that should be central to our thinking about infrastructures.In the following, I will first characterise the essential elements of the Roman decision-making regime before outlining, in a second section, the returns that I believe can be derived from applying the framework of "infrastructure." A Roman Paradox?There is probably no other political culture in the ancient world that would have been so concerned, at all levels, about holding individuals accountable for their actions.Every action required authorisation, which assigned it to a specific person.This strict model of representation was not only a deliberative setting that followed the logic of the political sphere; it was, moreover, a fundamental cultural disposition that applied in other segments of society as well.According to the Roman conception, every group, and every organisation needed a representative, an auctor, so to 1 For helpful discussions of the text, I like to thank Andreas Bendlin (University of Toronto).
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.018 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 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".