The Role of Migration Research in Promoting Refugee Well-Being in a Post-Pandemic Era
Bibliographic record
Abstract
This paper summarizes the presentations and discussions of a virtual stakeholder meeting on Refugee Resettlement in the United States which built on the foundation of the May 2019 workshop represented in this special issue. With support from the Robert Wood Johnson Foundation and the Andrew W. Mellon Foundation and hosted by the Committee on Population (CPOP) of the US National Academies of Sciences, Engineering, and Medicine on Dec 1–2, 2020, 1 the meeting convened migration researchers, representatives of US voluntary resettlement agencies, and other practitioners to consider the role of migration research in informing programs serving refugees and migrants during the COVID-19 pandemic, continuing an emphasis on bringing global learning to those on the ground working with refugees. The goal of CPOP's work in this area has always been to build bridges between communities of research and practice and to create a dialogue for a shared agenda. We present the goals and framework for the 2020 meeting, followed by a summary of each of the four sessions and themes that emerged from these discussions. The paper ends by considering effective ways of amplifying the role of research in refugee policy and programs of refugee resettlement in the United States and how demographers and population researchers might contribute to this goal.
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 | 0.103 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.023 | 0.035 |
| Scholarly communication | 0.018 | 0.016 |
| Open science | 0.002 | 0.020 |
| Research integrity | 0.007 | 0.011 |
| 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".