HE RELATIONSHIP OF MONEY TRANSFERS AND ECONOMIC GROWTH BY THE CASE OF ARMENIA
Bibliographic record
Abstract
In modern economic realities, foreign money transfers are an important source of financing for low- and middle-income countries. Migrant money transfers have been growing rapidly in the past few years and now represent the largest source of foreign income for many developing economies. While, not in far past, private foreign money transfers were viewed exclusively as a socially specific form of exchange of funds between individuals and households. After the ongoing of the Russian-Ukrainian conflict, the volume of foreign private money transfers inflows in the Republic of Armenia has sharply increased since the second quarter of 2022, which led in particular, to a rapid growth in private consumer spending in the short term, which in turn stimulated economic activity in non-exportable sectors of the Armenian economy, such as trade, construction, and services. Analyzing the dynamics of the indicators of net flows of external private transfers and the rates of economic growth in Armenia, it becomes quite obvious that in the years of high economic growth in the Republic of Armenia, a high growth of private external transfers was also recorded, and in the years when foreign private transfers decreased, economic growth was quite low or an economic decline was recorded. Accordingly, it can be concluded that the economic growth of the Republic of Armenia continues to be largely determined by the growth of non-exportable sectors stimulated by external factors, in which case, in the event of a decrease in the impact of external factors, economic growth sharply slows down.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".