Stories of teaching and learning, a memoir
Bibliographic record
Abstract
I am going to tell you a story in this document. It is my story. Some of these stories are about my experiences as a learner while others are from my perspective as teacher. Both are really stories about learning because I am a learner in my own classroom. I'm often learning different things from my students but I am learning with them and from them. In this way, I understand teaching and learning as opposite sides of the same coin: only the perspective is different. Most of what I know about teaching I either know through reflection on myself as a learner or from placing myself in the shoes of the learners in my classroom and attempting to understand their perspectives. This story spans many years. In many instances I understand the events differently as years of lived experience and numerous discussions have informed how I make sense. I believe, however, hat it would be a narrow and self-indulgent focus if these stories were only about me. They are also about you in the sense that these stories may provide a window through which you can see aspects of your own experience and self. To this end, I hope that this document is both a window and a contribution to the ongoing conversation around education.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.008 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".