Bibliographic record
Abstract
This article originated as a conference paper delivered by KS in May of 2011 at the conference "Money and Power in the Roman Republic", an event organized by Hans Beck, John Serrati and Martin Jehne at McGill University, Montreal.As it was not included in the conference publication, which was published as a volume in the Collection Latomus series in 2016 (only thirteen out of twenty-one papers read at the conference were selected for inclusion in the book), it remained an unfinished draft for a long time.In 2018, JL accepted an invitation to contribute to a fleshed-out version of the original paper.Her substantial and most significant input to the article earned her full co-authorship.The article has also benefited from several insightful comments and helpful suggestions offered by two anonymous readers for this journal, which is acknowledged with gratitude.1 Pol.1,1,5: τίς γὰρ οὕτως ὑπάρχει φαῦλος ἢ ῥᾴθυμος ἀνθρώπων ὃς οὐκ ἂν βούλοιτο γνῶναι πῶς καὶ τίνι γένει πολιτείας ἐπικρατηθέντα σχεδὸν ἅπαντα τὰ κατὰ τὴν οἰκουμένην οὐχ ὅλοις Arctos 52 (2018) 167-190
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".