When Stars Are Scattered. By Victoria Jamieson and Omar Mohamed
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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?
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it