Tracing the Link Between Transformative Education and Social Action Through Stories of Change
Bibliographic record
Abstract
The following article describes how one organization, the Coady International Institute, met multiple monitoring, evaluation, research, and learning objectives while still staying true to its roots in transformative adult education. The Learning from Stories of Change (LSC) methodology brought together stories-based techniques with aspects of the Most Significant Change and the SenseMaker frameworks. The combination of methods was designed to facilitate reflection and a degree of participatory analysis in an online environment that reached over 400 graduates in 64 countries. It produced a rich set of data that provided key insights into program design and confirmed the transformative adult education model—particularly, that increases in knowledge and skills must be accompanied by changes in attitudes and motivations in order to make the leap from concepts to practice. This leads to individual behavioral changes that will in turn initiate positive social change in communities around the world.
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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 | 0.015 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.007 | 0.032 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.002 | 0.005 |
| 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".