The Impact of Lebanon’s Multidimensional Crisis on Young Adults’ Political Attitudes and Behavior
Bibliographic record
Abstract
Since October 2019, Lebanon has been experiencing one of its most severe economic, financial and political crises, including economic depression, currency depreciation, insolvent banks, capital controls and political stalemate. This multi-dimensional crisis has had devastating consequences for the entire population, as manifested by increased unemployment, growing poverty and rising food prices as well as the emergence of medical supply and fuel shortages. Existing data from Western countries show a link between economic crisis and changes in political attitudes and behavior among citizens. Although research was conducted in Lebanon before and after the current crisis, no study to date adequately explores changes in political attitudes and behavior among young adults in particular after October 2019. This article examines the impact of this multidimensional crisis on young adults’ political attitudes and behavior. Based on interviews with a sample of eighteen young Lebanese between the ages of twenty-one and twenty-seven, we provide a qualitative analysis of how this multidimensional crisis impacted their political attitudes and political behavior. Our findings show that the crisis has led to an increase in political distrust, changes in political affiliations and a decrease in interest in politics among young adults in Lebanon. As information about changes in political attitudes and behavior among the youth amid the ongoing crisis in Lebanon is currently limited, the article provides important insight into the effects of the crisis on young adults’ perceptions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".