Bibliographic record
Abstract
This article engages with nine Myanmar-Burmese Buddhist nuns (thilashin) from three different nunneries in Sagaing, Myanmar, and examines their experiences with the monastic examinations. Because the nuns’ voices are frequently omitted from studies on monastic education, this article includes these perspectives and examines a few of the factors that contribute to the thilashin’s success in their education trajectories. In my research I find that responsibility, gratitude, and the Burmese concept of kyezusat—the return of gratitude to carers—plays a key role. I examine the nuns’ networks and ‘interlocking relationships’ between teachers and students. I additionally explore the active role that thilashin play in maneuvering their monastic kin into the different education systems that results in affective notions of kyezusat, and the responsibility for the monk or nun to want to return the gratitude to the one who took care of them. Furthermore, in order to understand monastics and their education, as well as Burmese Buddhist society, I advocate looking at the Singalovadasutta, in particular at Ledi Sayadaw’s version of this sutta, the Sukumaramaggadipani, and the suttas within the Mahavagga in the Pali Vinaya that focus on reciprocity. These texts highlight examples of students taking care of their teachers and the teachers taking care of their students that help influence Buddhism today.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.012 |
| Scholarly communication | 0.020 | 0.022 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.033 | 0.011 |
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".