Bibliographic record
Abstract
In teaching culturally sensitive and difficult issues, tensions and interruptions may arise, and educators and students may retreat to their respective comfort zones to avoid conflict and suffering, a pedagogical aporia occurs. This article introduces and examines Bodhisattva compassion from the Buddhist tradition, which offers insights and wisdom in transforming unexamined emotional responses into healthy and nonviolent expressions and embodiment of difference and dissonance. By tracing the Chinese etymological history of the term compassion and its use in Buddhist literature, I argue that Bodhisattva compassion embodies 悲心, a somatic, but unattached and awakened responsive heartmind. Bodhisattva compassion recognizes and accepts the unavoidability of human suffering, but it also liberates us from the common assumption of fellow-feeling and pity subsumed in sorrow and suffering. Guided by the concepts of wisdom and transforming the mind in Buddhism, bodhisattva compassion focuses on lucid awareness of one’s responsive heartmind and skillful actions to engage suffering. Pedagogy enlightened by bodhisattva compassion has curricular and instructional implications. In the struggle of identity politics or for social justice, it is probably more critical to develop ethical and undifferentiated compassion pedagogy than wrestling with power dynamics in our teaching.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".