National Identity Formation in the Baltic Provinces of the Russian Empire: The Skerst Family in the Nineteenth and Twentieth Centuries
Bibliographic record
Abstract
The modern history of the Baltic provinces of the former Russian Empire has essentially been written from an ethnic/national perspective. It is basically the story of the formation of the Latvian, Estonian, Lithuanian and German 'communities', of their 'specific' national identities and eventually of nation states. With those who acquired a German identity, the focus has essentially been upon the landed nobility, the so-called 'Baltic Barons', the traditional elite that formed a minority even of the ethnically German population. The existence of other German groups has been recognised, such as the 'literary estate' (Literatenstand), which in the nineteenth century 'brought into Baltic higher culture, rationalist viewpoints and represented a potential threat to noble control of local politics.' However, such groups have received comparatively little attention from historians, especially among those publishing in English. Even then there is limited acknowledgement of their possessing distinctive cultural and other forms of self-identification. A recent study by a Canadian scholar of the Germans of Riga before 1914 tends to impose the values of the landed elite upon them. In works published in post-1945 (West) Germany by emigres from the region, there is an inclination to present a distinctive 'Baltic German' identity that is largely derived from the experience of the landed elite.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".