When Stars Are Scattered. By Victoria Jamieson and Omar Mohamed
Bibliographic record
Abstract
When we hear stories about refugees, we tend to hear first about numbers. Big numbers. We hear that there are now more than 100 million forced migrants in the world. And while we have long known that there are problems with numbers (Crisp 1999; Krause 2022), we can still get lost in them. It can be especially hard for younger readers as numbers tend to obscure the actual experiences of refugees. This is also true when we think of individual situations, such as the Dadaab refugee complex in Kenya. Since its establishment in the early 1990s, Dadaab has been one of the most researched refugee situations in the world. While this work has brought us important new understandings of the politics of humanitarian practice (Hyndman 2000) and the coping strategies of refugees (Horst 2007), most of this research is not produced by people who live in Dadaab. We tend to only hear about Dadaab in fleeting moments, such as when the Government of Kenya threatens to close the camps or at times of acute need or crisis in the camps. What happens between these moments of attention? How can a younger reader begin to understand daily life for someone their own age in Dadaab?
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.058 | 0.026 |
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".