<i>Remembering the Dead: Collective Memory and Commemoration in Late Medieval Livonia</i> , by Gustavs Strenga
Bibliographic record
Abstract
In this engaging monograph, Gustavs Strenga examines the medieval commemoration of the dead in Livonia, both as a form of collective memory and a social practice. His focus is on the East European region of Livonia (sometimes called Old Livonia), which embraces the modern territories of Latvia and Estonia and was neither a medieval duchy nor a kingdom. Despite an increasing number of scholarly publications over the last decades, Livonia is an under-explored region in medieval studies. Although the timeframe of this book is late medieval, that is, in the case of Livonia, ‘from the early fifteenth to the early sixteenth centuries’ (p. 20), the text sometimes reaches back to the crusading period of the thirteenth century (e.g. the battle at Durbe, 1260); likewise, some chapters inevitably extend the discussion forward to the Reformation era of the mid-1520s. Strenga aims to analyse ‘not only the practice of remembering but also the impact of memoria on groups and relationships within groups’ (p. 19). In the introduction he claims (emphasising the key concepts of Memoriaforschung by Otto Gerhard Oexle) that ‘memoria is a form, a manifestation of collective memory’ (p. 25). This allows him to see the concept of memoria as applicable to groups and individuals who create the community which unites the living with the dead. These groups (or actually institutions) are represented by the Livonian Masters of the Teutonic Knights, the bishops and archbishops of Riga, followed by Cistercian nuns and Dominican friars; members of the merchant guilds of Riga and Reval (Tallinn); members of the Brotherhoods of the Black Heads in Riga and Reval, and also members of the beer carters’ and porters’ guilds in Riga.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".