Speaking of Learning…
Bibliographic record
Abstract
I have no doubt that many of you who read this book will be captivated by it, just as I have been captivated. This book is woven through evocative stories told by masterful educators who came together to explore the meanings of learning, teaching, and life. For those who have read Speaking of Teaching, it is not a surprise to hear, again, the profoundly touching, humane, and imaginative voices of these authors. This book draws me in, touches my heart, and refreshes my mind. —Hongyu Wang, Professor, Oklahoma State University, Tulsa, OK, US The authors invite us to join them in asking, “What else can learning be?” What else indeed? What is beyond the recipes, rubrics, formulas, and credentials of contemporary education? Deep in the heart of their own personal stories, told and untold, spoken and unspoken, the authors search and tell. With an artful admixture of stories, poems, artwork, and reflections, this book is a rare opportunity to listen in on an eight-year extended conversation amongst these gifted educators as they become increasingly present in their learning journeys. —Arden Henley, Professor and Principal, Canadian Programs, City University of Seattle, Vancouver, BC, Canada
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.006 |
| Insufficient payload (model declined to judge) | 0.030 | 0.023 |
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".