The Pedagogy of Suffering: Four Fragments
Bibliographic record
Abstract
This paper is a collection of small, formal and informal writings and is part of the early groundwork we have been doing together on the topic of the pedagogy of suffering, a phrase that has certainly given pause to many colleagues we have spoken to. We are trying to understand and articulate how and why suffering can be pedagogical in character and how it is often key to authentic and meaningful acts of teaching and learning. We are exploring threads from both the hermeneutic tradition and from Buddhism, in order to decode our understandable rush to ameliorate suffering at every turn and to consider every instance of it as an error to be avoided at all costs. We also look to these traditions to begin to formulate how a pedagogy that turns away from suffering suffers a great loss, and how a pedagogy that turns towards suffering can become a locale of great teaching and learning, great wisdom and grace.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.041 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 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".