Bibliographic record
Abstract
The consumer society of nowdays would have struggled to exist in the last century; money, which today carries no intrinsic value (legal tender), used to hold value by being linked with precious metals. The exchange rates of each country’s currency were determined by the quantity of precious metals each country possessed in tis central bank’s coffers. After the collapse of the fixed-rate system, the determinants of exchange rates have been hard to find. Given the continuous widening of the Albanian trade deficit, as well as the continuous strengthening of ALL against Euro, this study aims to investigate whether fundamental factors affect the ALL-EUR exchange rate, at all. In addition, this study examines whether this exchange rate has been influenced by the Bank of Albania’s interventions in the (domestic) foreign exchange market. The study is based on a quantitative analysis, with secondary data obtained from INSTAT and the Bank of Albania. The data are quarterly and have been collected for a period of 14 and a half years, from the first quarter of 2008 to the second quarter of 2022. The graphical analysis and regression results showed that fundamental factors significantly affect the ALL-EUR exchange rate and that the interventions by the Bank of Albania, in the foreign exchange market, have not had a statistically significant impact on the (domestic) exchange rate.
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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 | 0.001 | 0.001 |
| 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) | 0.003 | 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".