Re-actualization of scientific studies of ethnological institutions of the Ukrainian Academy of Sciences of the 1920s of the ХХ century (to the centenary of the establishment of the Cabinet of Anthropology and Ethnology named after F. Volkov)
Bibliographic record
Abstract
In the context of the development of ethnological science in the first quarter of the 20th century, the article exa-mines the current and still relevant methods of ethnographic research on the example of the scientific work and creativity of the Ukrainian artist-ethnographer Yu. Pavlovych. His legacy includes tens of thousands of sketches of the monuments of the ethnic culture of the Ukrainian and other peoples, and includes original scientific publications. The research interests of the ethnographer were formed as a result of his acquaintance with the works of the leading Ukrainian scientists P. Chubynsky, F. Volkov and V. Antonovych. Since the time of Yu. Pavlovich’s work in the Cabinet of Anthropology and Ethnology of the Academy of Sciences of Ukraine named after F. Volkov, his ethnological activity acquired consistency and was regulated by the ethnographic scientific projects of this institution. According to these programs, the scientist participated in monographic studies of settlements, successfully mapped the phenomena of material culture and carried out field ethnographic research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.007 | 0.024 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| 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".