Bibliographic record
Abstract
Blog post with references Published on https://rebeccanordquist.edublogs.org/2025/09/19/tell-me-a-story/ Sometimes the strands of life combine in unexpected ways; this summer unexpectedly became the Summer of Storytelling for me. It started with the One Book One Campus that Utrecht University is running this year (for the second year) where everyone is invited to read and discuss a book. This year´s book is Birnham Wood which is hard to summarize, but for me the energy of the community group that the book centers on, who are trying to protect and restore the land they are living on, was what really stayed with me. And then summer vacation came, and I found myself driving through Scotland, and passing…. Indeed, the Birnham Wood, the one that Shakespeare refers to. Tickled by this coincidence, we continued down the road to our next rest stop, at the Pitlochry Dam Visitor Centre. Which brought me to my next story: I had been listening to the audiobook “Drinkable Rivers” by Li An Phoa, not only a force of nature but a beautiful storyteller (the audiobook read by Li An Phoa herself is extremely worthwhile, for those who speak Dutch), where she tells the story of the effects of power turbines built in Canada on the nature and people living in the river´s path. And as much as I support transitioning to sustainable energy sources, her book had me look at the dams, built to supply electricity. Looking out across the river from atop the Pitlochry Dam, I asked myself what the people living here thought about the dams; and indeed, on the way to Glen Affric, where a large section of the hydropower is being built, there were large protest signs: “We are not the SSEN industrial junction”, and ecologists are worried about effects of hydrostorage on the lochs. And all of this came on the heals of listening to an incredible storytelling duo: Dougie Mackay and Jemima Thewes, who as actual, professional storytellers, kept a room full of people spellbound with the storytelling session “A wolf shall devour the sun”, which was so incredible and timely for Dutch current events with wolves, that I have told just about everyone I know about it, and I´m going to return to that in another post. All of these things came together to point me toward the power of storytelling. And as these things go, I wondered how I might be able to use that for teaching; if storytelling made such an impression on me, is it something that might work for students? I have been thinking about alternative routes for reflection, after reading the Reflective Zombies paper I posted about some time ago, and hearing one of my students literally say “they want to hear about how it made you cry”. If that is what our reflections are doing now, maybe it is time for change. And also as these things go, fortunately, I am not the first person to wonder if storytelling might work for reflection. This led me to a new reading list: on how to use storytelling in professional education by Zaitseva and colleagues (2024) and McDrury and Alterio (2021); working with storytelling in sustainability work by Talgorn and Ullerup (2023). The innovations with digital storytelling by Tondrow and Ong (2025), which I am finding interesting and potentially very attractive, but also daunting. Tell me a story. Tell me your story. Tell me what the story means to you. Tell met the story you want to share. I´m looking forward to where this storyline leads. McDrury, J., & Alterio, M. (2001). Achieving Reflective Learning Using Storytelling Pathways. Innovations in Education and Teaching International, 38(1), 63–73. https://doi.org/10.1080/147032901300002864 Talgorn, E., & Ullerup, H. (2023). Invoking ‘Empathy for the Planet’ through Participatory Ecological Storytelling: From Human-Centered to Planet-Centered Design. Sustainability, 15(10), 7794. https://doi.org/10.3390/su15107794 Towndrow, P. A., & Ong, M. W. L. (2025). Digital Storytelling as a Reflective-Reflexive Practice for Teachers’ Professional Learning. In Teacher Professional Learning (pp. 261–279). Springer, Singapore. https://doi.org/10.1007/978-981-96-4714-9_17 Zaitseva, E. V., Goncharova, N. V., & Daineko, L. V. (2024). Storytelling in Higher Education: A Literature Review. Scenarios, Fictions, and Imagined Possibilities in Science, Engineering, and Education, 3–31. https://doi.org/10.1007/978-3-031-76800-2_1
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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.397 | 0.356 |
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".