Economic and Technical Modeling of the Lebanese Crypto Currency: Implication for a Digital-Lira (DL)
Bibliographic record
Abstract
Since 1997, the Lebanese pound has been pegged to the U.S dollar at a fixed rate, and at every crisis, people rush to banks to convert their LBP accounts into U.S.D causing a high demand for foreign currencies. Fear and uncertainty finds its way to the market, and the general sentiment often causes various sectors of the economy to be negatively affected. Lebanon is highly dependent on the U.S.D and the LBP had slowly become nothing but a symbol of independence and a heritage that tells the story of “Lebanon”. This paper will examine the century’s most aspiring technology to see how it can be implemented to create a new form of national currencies, and how such a technology can incorporate basic to complex monetary policies in an automated manner to gain value, and have a controlled inflation through a pre-programmed system. This paper will also break down the “Bitcoin” conditions and show how it can be modified to fit a locally produced national crypto-currency for Lebanon that will be referred to as “Digi-Lira”. Finally, this paper will highlight the economic impact of the Digi-Lira on demand, investment, international trade, remittances, the unbanked population, and the banking 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".