LEARNING IN/DEPENDENTLY IN REFUGEE CAMPS: COMMUNITY-BASED PERSPECTIVES ON TEACHING, LEARNING, AND TECHNOLOGY
Bibliographic record
Abstract
The focus of this project is to understand the ways in which teaching, learning, and technology interact in/dependently in the daily lives of refugee people in Dzaleka Refugee Camp at home, in the community, and at school. Distinctly, we are asking questions about the role of technology in the everyday lives of refugee people in Dzaleka, and specifically related to how teaching and learning relationships are enacted with, about, and around tools that are of value to community members. This AoIR paper will be framed around two key components of this work. The first pertains to the methods in place, specifically, participatory qualitative research methods using remote, digital data collection. The second area of focus is on the preliminary findings from data collection underway between April-July 2021, based on the socio-technical exploration of teaching and learning with technology in Dzaleka. Our study, at present, focuses on three settings: online learning, music production and DJing, and sewing. This work sheds light on novel, in/dependent forms of teaching and learning in these areas in one refugee camp. And this work is needed to inform future technology initiatives in those settings from a community based perspective.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.017 | 0.021 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".