Storytelling in University Education: Emotion, Teachable Moments, and the Value of Life
Bibliographic record
Abstract
Teaching as a process of storytelling allows faculty to reach out to students' emotions, breathing life into the ideas that are being taught and learned. Storytelling among indigenous peoples emphasizes the importance of balanced relationships with the land, with all living creatures, and with the cosmos. It serves different functions in other societies that can also enhance teaching and learning. Alfred North Whitehead's account of emotions is relevant to the learning process and to teachable moments better understood as learning opportunities that enhance the value of students' lives. I provide an example of such a learning opportunity in which one of my students experienced an energetic connection with her colleagues as a living community. Teachable moments and storytelling can strengthen students' range of comprehension, their imaginative and aesthetic appreciation, as well as their growing capacity to learn. True stories - those based on a process which more coherently and inclusively takes into account life coordinates - enable students and faculty to critique the money sequence of value of the global market. A pedagogy based on true storytelling opens up spaces for the expression of different groups whose voices have been marginalized.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".