Refugee Resettlement: Why a Computational Method using E-CARGO is Better?
Bibliographic record
Abstract
Refugee resettlement (RR) is a crucial component of a comprehensive response to forced displacement. Refugees bring diverse skills, talents, and perspectives, enriching the cultural and economic fabric of their host communities. Allocating refugees to communities where their needs are adequately met, and they can best use their skills will contribute to the wellbeing of both refugees and host communities. In this paper, we propose that computational methods can drastically reduce the cost and improve the efficiency of refugee allocation in Canada. We present an initial simulation to demonstrate that using computational methodologies to allocate refugees systematically is better than conventional manual work. The proposed simulations suggest assignments of refugees by maximizing overall evaluation values. The Role-Based Collaboration (RBC) methodology and its Environments - Classes, Agents, Roles, Groups, and Objects (E-CARGO) model have been verified to be a promising method for simulating social phenomena. This work uses RBC/E-CARGO to model the RR problem and obtains the RR results by simulations. These simulation results can also reflect the current manual resettlement experiences. This methodology is innovative and original. It is the first trial using RBC/E-CARGO in dealing with RR problems.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".