A Conceptual Framework of Displaced Elderly Syrian Refugees in Lebanon: Challenges and Opportunities
Bibliographic record
Abstract
In the context of ongoing armed conflicts, efforts to provide humanitarian care are often not sustainable or effective in the long run. Additionally, there is a significant gap between interventions that are theoretically feasible and those that are actually implemented in practice. Building on these foundations and challenged by the limited publications on Syrian refugees, especially the elder population, we explore the understudied connection between the day to day elder refugee experience on one hand and the lack of building resources from within on the other. We take the example of Lebanon, where as many as 4000 Syrian refugees crossed into its territory daily and which now has the highest number of refugees per capita in the world. Lebanon has limited resources and funding and is strained under this socioeconomic burden. Due to this harsh reality, refugees’ simplest needs are largely unmet and they are easy targets for retaliation by local civilians competing for basic resources. Needless to say, elderly refugees suffer most from these inequities and their status is particularly vulnerable. Within this context, and based on ongoing fieldwork, we offer a conceptual framework which calls for effective and sustainable interventions nurturing resilience in elderly refugees and ultimately aiming to help decrease tensions between the host communities and refugees.
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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 | 0.007 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.011 | 0.028 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".