Bibliographic record
Abstract
Education plays a significant role in informing the way people develop gender values, identities, relationships, and stereotypes. The education of refugees, however, takes place in multiple and diverse settings. Drawing on a decade of field research in Kenya, Sudan, Uganda, and North America, I examine the promises and challenges of education for refugees and argue that southern Sudanese refugee women and girls experience gendered and unequal access to education in protracted refugee sites such as the Kakuma refugee camp, as well as in resettled destinations such as Massachusetts. Many of these refugees, who are commonly referred to as the “lost boys and girls,” did not experience schooling in the context of a stable family life; that is why they often reiterate the Sudanese proverb, “Education is my mother and father.” I argue that tertiary education is crucial because it promotes self-reliance. It enables refugees, particularly women, to gain knowledge, voice, and skills which will give them access to better employment opportunities and earnings and thus enhance their equality and independence. Indeed, education provides a context within which to understand and make visible the changing nature of gender relationships of power.
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.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.032 | 0.011 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".