Bibliographic record
Abstract
I read the book under review in Polish translation some ten years ago.When I returned to it in autumn 2021, this time in English, I remembered only that it had represented an important undertaking that reinforced my decision to focus on pre-twentieth-century Ukrainian history in my academic career.Other than that, I had only vague memories and shades of apprehension.I wondered at that time, Would this work prove to be equally stimulating and satisfying after ten years of personal and academic development?I thought, Would I risk enduring a painful embarrassment-what German speakers call Fremdscham, that is, a sense of shame for another personʼs actions?I worried, Would I be forced to execute an ugly written Vatermord ("parricide")?Admittedly, it is in bad taste to start a review of someone elseʼs book by writing about oneself.But I believe that my personal experience of revisiting Iaroslav (Yaroslav) Hrytsakʼs monograph gives a good illustration of how powerful and transformative the work actually is.Upon rereading the volume, I was able to appreciate the changes that had taken place in several fields of historical study from the time that the book was first published, in
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".