The Dynamics Between Banking Performance & Economic Stability in Lebanon
Bibliographic record
Abstract
The Lebanese banking sector has traditionally been one of the foundation pillars of the economy; however, extraordinary challenges have arisen from sources of macroeconomic volatility and political uncertainty compounded by inherent systemic weaknesses. This study methodologically ranges from hybridization of both qualitative insights with quantitative data. The analysis is based on a survey among the stakeholders in the Lebanese banking sector and triangulated with data from both the Central and commercial banks. The findings indicate that currency devaluation significantly reduces the real value of banking assets, increases credit risk, and raises non-performing loans, therefore undermining profitability and systemic stability. In addition, increasing liquidity pressures, further exposed by declining deposits and increasing capital flight, highlight the fragility in public confidence in the financial system. All these weaknesses have been compounded by the lack of strong regulatory frameworks and the slow introduction of capital controls, creating a feedback loop where economic instability reinforces systemic weaknesses. This study has shown the requirement for consolidated policy interventions that will stabilize exchange rates, reinforce regulatory oversight, and revive depositors' confidence.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".