A foreign population marking its presence in Poland between the 10th and mid-11th centuries: research conditionings, problems and perspectives
Bibliographic record
Abstract
When it comes to research into changes which took place in Poland in the 10th and the first half of the 11th century and the emergence of the Piast dynasty’s state, the presence of an ethnically foreign population, its conditioning and effects have not been fully recognised. The few historiographical sources do not devote much attention to the arrival of foreign tribes; the single mentions typically pertain to the representatives of the elites, especially dynasties. Attempts have been made to analyse the phenomena by means of toponomastics and archaeology. Due to their ambiguity and late source confirmations, the results of toponomastic surveys do not allow to resolve the issue of migrations or displacement from the 10–11th centuries independently. However, the archaeological research carried out to date has revealed (beside a number of single historical objects related to the culture of Poland’s southern neighbours) grave fields and strongholds which could be potentially related to the representatives of foreign ethnic groups. The Poznan-Solacz grave field (2nd half of the 10th century) and the Morawy grave field in Kuyavia (2nd half of the 11th century or possibly earlier) are related to a population from (Great) Moravia. Presence of a Hungarian population is traditionally attributed to the “old Hungarian” grave field in Przemyśl-Zasanie (dating back from the late 9th to the first quarter of the 11th century). On the other hand, the stronghold and the grave field in Niemcza in Silesia (dated back to the 970s and 980s) are connected with a Czech population. Unconfirmed grave fields and a handful of artefacts of Great Moravian origin are typical remains of strongholds in Gilow in Silesia and Czerchow near Łeczyca where presence of foreign warriors has not been ruled out. A question remains to what extent the material determinants of a foreign culture indicate presence of representatives of different ethnic groups and to what extent they are imports or copies. Undoubtedly, in a discussion of a foreign population genetic research may prove helpful, especially in grave fields associated with foreign populations. However, in order to analyse the issue in a comprehensive way, an interdisciplinary approach is required i.e. a combination of the methods of historical, archaeological and genetic research.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".