Mobilizing Global Knowledge: Refugee Research in an Age of Displacement
Bibliographic record
Abstract
In 2018, the United Nations High Commission for Refugees documented a record high 71.4 million displaced people around the world. As states struggle with the costs of providing protection to so many people and popular conceptions of refugees have become increasingly politicized and sensationalized, researchers have come together to form regional and global networks dedicated to working with displaced people to learn how to respond to their needs ethically, compassionately, and for the best interests of the global community. Mobilizing Global Knowledge brings together academics and practitioners to reflect on a global collaborative refugee research network. Together, the members of this network have had a wide-ranging impact on research and policy, working to bridge silos, sectors, and regions. They have addressed power and politics in refugee research, engaged across tensions between the Global North and Global South, and worked deeply with questions of practice, methodology, and ethics in refugee research. Bridging scholarship on network building for knowledge production and scholarship on research with and about refugees, Mobilizing Global Knowledge brings together a vibrant collection of topics and perspectives. It addresses ethical methods in research practice, the possibilities of social media for data collection and information dissemination, environmental displacement, transitional justice, and more. This is essential reading for anyone interested in how to create and share knowledge to the benefit of the millions of people around the world who have been forced to flee their homes.
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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 | 0.022 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.032 | 0.058 |
| Scholarly communication | 0.038 | 0.050 |
| Open science | 0.003 | 0.045 |
| Research integrity | 0.014 | 0.021 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".