Narrative Imagination and Social Change: Instructors in Agricultural Colleges in Ethiopia Address Sexual and Gender-Based Violence
Bibliographic record
Abstract
Ethiopia has one of the highest rates of sexual and gender-based violence (SGBV) in the world, making female students particularly vulnerable in its post-secondary institutions. Although there is extensive literature that describes the problem, mainly from the students' perspectives, what remains understudied is the role of instructors, their perception of the current issues, and what they imagine they can do to address campus-based SGBV, particularly in rural settings. In this study, we used the concept of narrative imagination to work with instructors in four Ethiopian agricultural colleges to explore how they understand the SGBV issues at their colleges and what they imagine their own role could include in efforts to combat these problems. Using qualitative narrative-based methods such as interviews and an interactive storyline development workshop, as well as cellphilming (cellphone + film) as a participatory visual method, the data were collected across several fieldwork phases. We consider how we might broaden this framework of narrative imagination to include the notion of art for social change.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.013 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".