The risk of a decline in profitability of a physical education and sports company during pandemic
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Довгалюк Иляна Михайловна, кандидат экономических наук, доцент кафедры туризма и социально-культурного сервиса, Южно-Уральский государственный университет. 454080, г. Челябинск, проспект Ленина, 76. E-mail: dovgaliukim@susu.ru, ORCID: 0000-0003-4398-8007. Савельева Ирина Петровна, доктор экономических наук, профессор кафедры бухгалтерского учета, анализа и аудита, Южно-Уральский государственный университет. 454080, г. Челябинск, проспект Ленина, 76. E-mail: savelevaip@susu.ru, ORCID: 0000-0001-6305-6413. Балахонова Лада Дмитриевна, студент группы ЕТ-134, Южно-Уральский государственный университет. 454080, г. Челябинск, проспект Ленина, 76. E-mail: balahonovalada@yandex.ru, ORCID: 0000-0001-5760-6159. Трофименко Елена Юрьевна, кандидат экономических наук, доцент кафедры менеджмента, Южно-Уральский государственный университет. 454080, г. Челябинск, проспект Ленина, 76. E-mail: trofimenkoei@susu.ru, ORCID: 0000-0002-7834-5311. I.M. Dovgalyuk, dovgaliukim@susu.ru, ORCID: 0000-0003-4398-8007, I.P. Saveleva, savelevaip@susu.ru, ORCID: 0000-0001-6305-6413, L.D. Balakhonova, balahonovalada@yandex.ru, ORCID: 0000-0001-5760-6159, E.Yu. Trofimenko, trofimenkoei@susu.ru, ORCID: 0000-0002-7834-5311 South Ural State University, Chelyabinsk, Russian Federation
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.
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 it