Women of the Horn in the diaspora: From victims to powerful actors
Bibliographic record
Abstract
For more than two decades the number of people from the Horn of Africa seeking \nrefuge, abroad, has increased dramatically. Several conditions such as armed conflicts, \nlack of democracy and widespread human rights abuses in these countries have generated substantial refugee flows to neighbouring countries such as Kenya and Tanzania as well as to remote destinations such as Europe, the USA, Canada and Australia. \nAll have contributed to the emergence of new Diaspora populations. \nThese new Diaspora populations have faced many problems and challenges. This \npaper highlights problems and obstacles facing women of the Horn in Diaspora. \nThese problems include the hostile environment towards refugees that has been perpetuated by the right wing media in the West. The paper also explores challenges facing women of the Horn in Diaspora and how they managed to tackle them. It also \nlooks at the positive role that they play to build up their countries and to promote \npeace, security, human rights and development.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.010 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".