A Hard Road from Home: Music and Fashion | Al Jazeera World
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
"Refugees are always connected. Once you become a refugee ... it's like the energy just pull you guys together." - Emmanuel Jal, musician Four people - refugees from Sudan, Iraq, Sierra Leone and Zimbabwe - took very different, unpredictable paths away from conflict or political persecution in their home countries. But they all have something in common: they have since built successful careers in the arts and culture industry and are driven to express themselves by using their skills to help others. Emmanuel Jal does not know when he was born. He knows he was born sometime in the 1980s in southern Sudan. He was displaced by the pre-secession war that took place between the north and south and was recruited as a child soldier while attending school in Ethiopia. He is now a critically acclaimed musician and author in Canada. Tara Moneka, a teenage singer from Baghdad, took part in a popular TV talent show in Iraq and received threats from militiamen angered by her singing. She now lives in exile in Turkey with hopes of returning home. Euphemia Sydney-Davies, who fled civil war in Sierra Leone as a child, today has her own fashion label in London, producing ethically sourced clothes for clients throughout the world. And Faith Gakanje, a vocal opponent of the government of Zimbabwe, fled her native country in 2002. She now lives in the UK, where she is a fashion entrepreneur and founder of a forum that supports refugee women. The four artists have found success through their determination, family support and what they consider to be a certain degree of good fortune. It is a gift they now pay forward to help fellow refugees and connect with others.
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 | 0.000 | 0.000 |
| 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) | 0.192 | 0.009 |
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