Влияние макроэкономических факторов на уровень продовольственных трат жителей Санкт-Петербурга
Bibliographic record
Abstract
The topic of this research is the impact of the macroeconomic factors on food spending residents of St. Petersburg. The problem of the study is the ambiguity of the food sanctions impact on the consumer spending on food. The aim of the work are the trend analyze in the level of food spending in St. Petersburg since the first quarter of 2007 to the third quarter 2015, and the identifying of factors influencing these food waste. St. Petersburg was chosen because it is a city with a large population, which is located in the European part of Russia, it means that its inhabitants often bought European food products before the embargo. During the research the following tasks were performed: the description of the embargo’s chronology from 2014 to 2016; the definition of the basic mechanisms of behavior of contractors doing business in the food sector; Econometric analysis of the food spending dependency on the embargo, the GDP change, the cost of Brent crude oil and the Consumer Price Index. The following results were obtained: the availability of food embargo and the oil prices rising increase food waste. The GDP growth, in contrast, reduces the spending level. Influence of the Consumer Price Index reflects on food spending in different ways, depending on the product category. In addition, the forecast of expenditure on food in the III quarter of 2016 was created by the main research model. Thus, the study clearly showed that the embargo significantly increases consumer spending on food, despite the fact that its purpose is the import substitution. Actually, consumers finance import substitution in the agricultural sector.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.006 |
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".