The 2018 Parliament of the World's Religions: Reflections on Approaches for Interfaith Dialogue
Bibliographic record
Abstract
In November 2018, 6 students from Dr. Claude Stulting's Senior Seminar on Religious Pluralism, attended the Parliament of the World's Religions held in Toronto, Canada. This was a gathering of some 10,000 people from all over the world and from virtually every religious tradition practiced around the globe. Under the guidance of Dr. Stulting and JoAnn Borovicka (author, teacher, leader in the Greenville Baha'i community), these students helped to run a Parliament workshop demonstrating an approach to interfaith dialogue called the Amazing Faiths Dinner Dialogue. Sponsored by the Furman University; the Interfaith Forum of Greenville; the Interfaith Conference of Greater Milwaukee; and the Buckminster Fuller Institute, this Parliament workshop was an opportunity for the students themselves to learn how to serve as moderators for the small groups characteristic of this method of interfaith dialogue. In serving as moderators for small groups of workshop participants, the students themselves enabled a first-hand experience of how it is possible to mindfully listen to others and understand their faith perspectives. The six students on this panel will each share their experiences of serving as moderators for this workshop. They may also include reflections on what they learned from attending the rich offering of sessions offered during the 5 days of the Parliament.
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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 | 0.032 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.049 | 0.032 |
| Scholarly communication | 0.019 | 0.013 |
| Open science | 0.004 | 0.020 |
| Research integrity | 0.011 | 0.021 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".