Destruction of religious monuments as a result of turkish-tatar raids on ukrainian lands, in the first quarter of the 17th century
Bibliographic record
Abstract
For Ukrainian lands, especially the western region, the end of the 16th – the first quarter of the 17th century was a time of tremendous devastation due to constant Turkish-Tatar raids. The destruction of whole villages and towns, houses and churches, massacres, and kidnapping accompanied the attacks by Turks or Tatars.The attacks of Turks and Tatars were not accidental, and there were several reasons for this. First, it is the desire to seize material values at the expense of the prisoners to increase their army and get free labour. However, there were political motives for such attacks. First, the reason was the complex relationship between the Polish-Lithuanian Commonwealth and the Ottoman Empire. The following factors were decisive in the next political reasons: interference of Polish magnates in the internal affairs of the Danubian principalities (in 1612, 1616, and 1619); the pro-Austrian policy of King Sigismund III of the Polish-Lithuanian Commonwealth and his entourage; annual attacks of the Zaporozhian Cossack Army on the Turkish coast and fleet. According to the historiographic and source analysis, as soon as the diplomatic relations between the Polish-Lithuanian Commonwealth and the Ottoman Empire intensified, the number of attacks increased dramatically. Especially many of them took place on the eve of and during the Khotyn War of 1620-1621. It happened because the Ottoman Empire intended to weaken the position of the Polish-Lithuanian Commonwealth and intimidate the local population. The greatest intensity of Tatar raids on Red Ruthenia occurred in 1612-1624. Mainly many attacks were carried out on the land of Galicia: 20 times were attacked Kolomyia district; 18 – Halytskyi; 17 – Terebovlianskyi; 14 – Lvivskyi; 12 – Zhydachivskyi; 9 – Stryiskyi; 7 – Drohobytskyi; 6 – Przemyslskyi; 5 – Sambirskyi.Destroying villages and towns, Tatars often chose religious buildings as the main object of attacks: churches, monasteries, and chapels. The enemy had several reasons for this: the Ukrainian population often used the church as a last refuge, hoping that the enemy would not burn down the shrine; the destruction and looting of the church brought material gains to the Tatars because in the churches there were always many valuable things that could be sold or melted down; the destruction of churches also had a psychological effect, people believed that the saints would be able to protect them, so often the main population of the village was concentrated in the church, during the raid of Tatars. As a result, they became easy prey for the enemy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".