Mock Research Proposal on Unregistered Syrian Climate Refugees in Jarraheih Camp of Bekaa, Lebanon
Bibliographic record
Abstract
Climate change is one of the greatest challenges of the 21st century, displacing a global annual average of 26.4 million people due to climate-related disasters. Currently, over 1.1 million Syrian refugees have fled to Lebanon due to climate-related drought and subsequent war, with 320,000 who remain unregistered by the UNHCR. Unregistered refugees are restricted from accessing services, shelter, and financial means for survival and risk arrest, detention, and deportation by Lebanese authorities for not having proper documentation and paid residency fees. These consequences are felt the most in refugee camps along the Syrian-Lebanese border region such as Bekaa, Lebanon. Despite the presence of humanitarian aid, refugees in camps are left in legal limbo, deprived of accessing the right to have rights. Specifically, for climate refugees, international treaties or future mitigation mechanisms are lacking, exacerbating their vulnerability. Therefore, this research questions the physical and social experiences of unregistered Syrian climate refugees in the Jarrahieh camp, Bekaa, Lebanon. The intention is to determine how these refugees, without official status, are impacted and coping with current federal and international legislative measures. By working with the refugees and key local actors, this research aims to understand how solidary and community organization can be formed within the current legal system to facilitate smoother adaptation and resettlement for climate refugees. Using participatory observation, focus groups, semi-structured interviews, and archival research, the practical outcome is a solidarity group that is established, run, and evaluated by a sample of unregistered Syrian climate refugee men and women aged 18-65.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.023 | 0.002 |
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".