Teresa Chynczewska-Hennel, One World – Many Colours, Zakład Wydawniczy Nomos, Kraków 2019, 203 ss.
Bibliographic record
Abstract
The work consists of an introduction and ten essays that both reflect the author’s extensive research interests and testify to her extremely rich and multi-coloured personality. It should also be noted that the discussed collection of studies by T. Chynczewska-Hennel in the vast majority was already rated very positively by American, Canadian, Italian, French, and Ukrainian historians, as it consists of articles that previously appeared in foreign publications. The essays presented in this book offer a highly focused overview of the numerous problems which were at the centre of Chynczewska’s scientific interests and which were thoroughly and clearly examined in her books. They also offer shining evidence of the author’s constant ethical engagement, her tireless search for the many points of contact between cultures and narratives which were opposed by cynical political goals, human envy, thirst for power, as well as by the inability of numerous leaders and social groups to look for genuine human values and interests that should have overridden their own egoism and the short-term interests of a specific group or class in the near-sighted “here and now”. To sum up, it should be noted that the reviewed work is not only of high scientific value but also intellectually inspiring. Other values of the book that should be highlighted here are, on the one hand, easier access to the author’s sometimes difficult to obtain texts and, on the other, the ability to accurately trace her extremely accurate insights on various issues.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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".