Teachers for Teachers: Advocating for Stronger Programs and Policies for and with Refugee Teachers in Kakuma Refugee Camp, Kenya (Dispatch)
Bibliographic record
Abstract
Teachers are expected to facilitate learning for children and youth, attend to their students' diverse academic needs, and provide emotional support and encouragement, while simultaneously meeting the expectations and demands of students, families, colleagues, school leaders and the larger education system.In crisis-affected and displacement contexts, teachers are regularly expected to perform these duties with little to no training, support or materials (Burns & Lawrie, 2015).Jackie Kirk, along with her colleague, Rebecca Winthrop, captured the inherent tension of this situation and the uncertain sense of identity that teachers in these contexts experience.Kirk and Winthrop (2007, p. 719) proposed the ideas of "spontaneous teachers," who become teachers reluctantly given the circumstances, and "tentative teachers," who feel uncertain about serving as teachers or remaining in the profession.Their conceptualization of teacher identity rings true for the teachers that my graduate students and I have been working within Kakuma refugee camp in northwestern Kenya since 2013.A similar sentiment is captured in the following quote from a young refugee teacher in the camp from South Sudan: Yes, I'm happy to be a teacher, but with a condition.If I can get training and prove that I'm a teacher, that's when I will tell that I'm happy to be a teacher.But as per now, I have not yet known the sweetness of being a teacher because I have not been trained….As per now, I don't call myself a good teacher.I am not good because I am not a professional.(Male teacher, South Sudan, July 2015)
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.019 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.032 | 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".