Teacher Education in a Refuge Context: An Exploration of the Challenges and Discoveries while Engaging in Academic Humanitarianism
Bibliographic record
Abstract
Dadaab, Kenya is known as the site of the world’s largest protracted refugee camp and has been inhabited continuously since January 1991. To respond to the education needs of those living in Dadaab, The University of British Columbia, York University, (Canada), Kenyatta University and Moi Universities (Kenya) collaborated to deliver teacher education programs that would lead graduates to meet Kenyan standards for teacher certification. In 2017, only 2% of youth ages 14-17 were enrolled in school, and of them, only one third of those enrolled are girls. There are not enough qualified teachers to teach the 98% of youth who should be in school. A key goal of this project (funded by Global Affairs Canada) was to improve the quality of secondary education in Dadaab by providing university education to the ‘untrained’ teachers (those teaching without any prior teacher education), and increase attendance of secondary students, in particular girls. What happens when one provides free teacher education to refugee students in situ? The focus of this presentation will be on examining some of the challenges faced during delivery and program impacts on graduates, instructors, and collaborating institution. Implications for further study, and future program development will also be discussed.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.032 | 0.018 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".