Keeping It Real: Decolonizing Christian Inter-Religious Practice as an Exercise in a Practical Theology of the Cross
Bibliographic record
Abstract
“What is suffering? What is hope?” These are questions I have asked for years with classes full of students training for Christian ministry. Now, I ask these questions in classes with Buddhist, Muslim, Jewish, Christian, and ‘spiritual but not religious’ students, all in training to be spiritual care therapists. The institution where I serve is in the process of transitioning from a mono-religious Christian theological College to a centre for multi/inter-religious education. Those of us who teach in the program are disrupted continually by pedagogical challenges that both perplex and energize us. The multi-religious classroom decolonizes spaces long dominated by Christian theological discourse. Course content yields to a fluid and open-ended, interactive process. My “mastery of the field” gives way to an ongoing practice of surrender—a kenotic self-emptying—that usually leaves me shaken in overwhelming awe or angst-ridden questioning. Through a practical theological methodology that begins with lived human experience, this paper shares an autoethnographic account of my experience as a teacher in the multi-religious classroom. It presents key dimensions of the theology of the cross as an interpretive framework and closes by examining how the theology of the cross offers a practical Christian theological reflective process to empower decolonizing pedagogy.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.050 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".