Fashion photography in Ukraine: to the problem of source analysis
Bibliographic record
Abstract
The article analyzes a portion of the fundamental world and Ukrainian sources on the formation and development of the genre of fashion photography in Ukraine. A brief tour of the works of foreign scientists who fully or partially researched the genre of fashion photography of the XX-XXI centuries — Roland Barthes (1967), Nancy Hall-Duncan (1978), Margaret Maynard (2008), Eugenie Shinkle (2008) and Karena De Perthuis (2020) was conducted. The main aspects from which existing studies were carried out were studied. It is noted that the rise in popularity of the photographic genre in Ukraine occurred in the first quarter of the XX century, which is a main reason of the narrow number of scientific studies in the field, as well as the lack of academic recognition of the genre. The share of works of Ukrainian scientists on the topic of studying the genre of fashion photography - N. Chuprina and V. Batrak (2011), O. Trachun (2014), I. Skoryk (2023) and S. Tulchynska (2023) was analyzed. It is acknowledged that the Ukrainian scientific space contains an insufficient number of works devoted to fashion photography, which indicates the need for further expansion of the scientific analysis of this photo genre, systematization of its development and influence in the Ukrainian scientific field. The purpose of the article is to analyze international and Ukrainian scientific sources that cover the key details of the formation and development of the genre of fashion photography.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".