Leaders for the Liturgical People: Shaping Students for Ministry in the Twenty-First Century
Bibliographic record
Abstract
In my first year of undergraduate studies at the University of Toronto, I enrolled in a course entitled “The Christian Imagination.” It introduced the students to Christian art across the ages, and it was taught by a very clever and energetic woman with a French accent. It was a mind-bending experience. In the course of the semester, Dr Langan showed us hundreds of slides of sculpture, painting and architecture. Quite apart from the incredible amount of material, it was the challenge to appreciate the great variety of expressions of the Christian experience that took my breath away. At the end of the semester, I vowed never to take another course in the Christianity and Culture program. It was just too stressful. Yet the next semester I found myself signing up for more of the same and after four years graduated with a major in the program. I have reflected upon what it was about this program which caught and held me. I believe that I was fascinated by the challenge to see Christianity from new and varying perspectives, to understand and evaluate the ways in which the Christian faith has been and continues to be lived in differing situations, and to imagine how the church might faithfully and creatively live the faith in the future. It was this fascination that brought me to the study of liturgy and which I dream of passing on to others.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.031 | 0.020 |
| Scholarly communication | 0.014 | 0.005 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.002 | 0.009 |
| Insufficient payload (model declined to judge) | 0.009 | 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".