Bibliographic record
Abstract
How small can microhistorians go? The article proposes the advantages of “particle history,” the intense investigation of small, often isolated and dislocated fragments, and how they connect to the worlds to which they once belonged. To demonstrate the method, the article takes a single stand-alone sentence, a colophon, from an early ninth-century manuscript, Brussels, Bibliothèque Royale de Belgique 8216-8218, in which the scribe, one Ellenhart, reports that he copied the book while on a military campaign and supplies the dates of his copying. This evidence leads the author and readers on a journey to reconstruct a military campaign to Hunia (Hungary) in 819, the reasons for that military venture, the nature of the army's travel, and the scribe's role and progress in making his book. But why was Ellenhart there at all and what did he choose to copy while on campaign? To answer those questions the author examines the special character and critical tensions of Carolingian monasticism and why the monk chose the lives and sayings of the desert fathers to copy while on campaign. From a single sentence in an obscure manuscript, a world of associations and connections opens, reminding us that microhistory is not reductive, as is sometimes claimed, but expansive, for when it works it connects its objects of inquiry to wider worlds of meaning and importance.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.012 | 0.010 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".