Conference Report: The Second Lotsawa Workshop, "Celebrating Buddhist Women’s Voices in the Tibetan Tradition"
Bibliographic record
Abstract
I n 2017 a group of esteemed Buddhist nuns from Larung Gar (བླ་རུང་སྒར།) Buddhist Academy in eastern Tibet published a groundbreaking collection of writings by and about Buddhist women in 52 volumes entitled Ḍākinīs' Great Dharma Treasury (མཁའ་འགྲོ འི ་ཆོ ས་མཛོ ད་ཆེ ན་མོ །).Inspired by this historic undertaking, Sarah Jacoby (Northwestern University), Padma 'tsho (Southwest Minzu University), Holly Gayley (CU Boulder), and Dominique Townsend (Bard College) organized the Lotsawa Workshop "Celebrating Buddhist Women's Voices in the Tibetan Tradition" at Northwestern University from October 13-16th 2022 in Evanston, Illinois.This was the second Lotsawa Workshop funded by the Tsadra Foundation, with support from The Luce Foundation/ American Council of Learned Societies and the Religious Studies and Asian Languages and Cultures Departments at Northwestern.This translation workshop explored a variety of questions surrounding the diverse range of Tibetan texts by and about women, including a wide array of genres and time periods stretching from classical Buddhist texts to modern Tibetan women's writings.Building upon the model of the first Lotsawa Workshop at the University of Colorado, Boulder in 2018, this second iteration spanned four days and likewise featured keynote addresses, panels, breakout sessions, and the flagship translation workshops.Whereas the first Lotsawa Workshop explored questions of translating devotion, piety, and related religious affects, this second workshop attended explicitly to the many issues at stake in translating gender across historical, cultural, and religious horizons.This focus drew together a robust international cohort of Tibetan and Himalayan women writers, established and emergent scholars and translators, Buddhist nuns, and Buddhist teachers and practitioners.Participants and panelists flew to Evanston from India, Malaysia, Bhutan, Canada, France, England, and across the United States to join in conversations, share writing, theorize translation, and unpack a host of gendered issues in a large variety of Tibetan texts.We were especially lucky to host and hear from two groups of inspiring and groundbreaking women.The first was a contingent of Buddhist nuns, including Venerables Tenzin Dadon (Vajrayana Buddhist Council of Malaysia), Ani Choyang (Northwestern University), Damchö
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.046 | 0.007 |
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".