Restor(y)ing hope: stories as social movement learning in ada songor salt movement
Bibliographic record
Abstract
Stories are a central component of how we understand ourselves and our societies in our world. This is especially true in the case of oral cultures. Stories, how they are used, how they are reframed, and how they change over time, are also an important record of learning. This article explores how a social movement in Ada, ghana, has been using stories to both learn and share that learning through several phases of struggle over the past six years. This movement aims to defend the 400-year-old communal artisanal salt production practice that is the livelihood of over 60,000 people. Women make up the majority of these practitioners. The aim of this paper is both to reveal the power of these stories for popular education and to explore how in restorying these stories over time the movement reveals the ongoing depth of learning. This paper also discusses how the alliance between the movement and the local community radio contributes to this restorying and learning.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".