Who is my Neighbour? Theological Education in the Global Village of the 21st Century (Responses)
Bibliographic record
Abstract
Chukwuemeka Anthony Atansi’s keynote address offered a series of reflections on how the Igbo theological anthropology of the neighbour can enrich theological education in diverse contexts such as Montreal. The Igbo theological anthropology of the neighbour offers a relational understanding of the neighbour, a relationality which comprises a crucial component of Karen Finch’s response, one which argues that good pastoral education in today’s polarized world strongly resembles good ecumenical formation. For Finch, ecumenical formation is about relationality and the dialogues that this relationality provokes, and she argues that, when approached with openness and transparency, such dialogues can be truly generative for pastoral education. Alyson Huntly’s response offers a series of reflections about the state of theological education in Quebec, reflections which also highlight relationality, or, more precisely, the need for Western institutions, hitherto predominantly white, to re-evaluate and re-envision how they relate to – and thereby dialogue with and learn from – the “Others” who now represent a majority demographic in Quebec’s theological colleges, many of whom come from the Global South or formerly colonized nations. This, she argues, represents the path to renewal for theological education in twenty-first century Quebec.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.019 | 0.013 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".