Your story, our story: the transformative power of life narratives
Bibliographic record
Abstract
The stories in this article opened the 2018 International Transformative Learning (TL) Conference. Mezirow founded this conference to grow living theory, but over time, paradoxically, allegiance to his framing of TL can be seen as limiting growth. The Conference designers invited participation from new geographical, cultural, social and political perspectives to bring forward unheard voices to create bridges among differences. Framed by the imaginal and holistic approaches to TL, this article shares the personal stories of transformation narrated by five panelists and the ‘wicked’ questions these stories evoked. The authors reflect on the humanizing, heart-ful space that sharing stories created, which re-minded those present of shared community. TL is about change in the individual, as theory seeks to explain; yet it is also about embedded social, cultural norms and assumptions that shape and sustain systems. The authors conclude by reflecting on the humanizing potential of narrative, as well as the theorizing about the interdependence between individual and systemic transformation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.017 | 0.057 |
| Scholarly communication | 0.023 | 0.025 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".