Insight, learning, and dialogue in the transformation of religious conflict : applications from the work of Bernard Lonergan
Bibliographic record
Abstract
A wealth of recent scholarship has focused on interreligious dialogue as a resource for the transformation of religious conflicts. Such studies often mention the importance of discoveries or 'insights' as key factors in successful dialogue processes. However, few authors have devoted sustained attention to understanding how insights contribute to transforming conflict dynamics during interfaith dialogues. The present study draws on the cognitional theory of Canadian philosopher Bernard Lonergan as a framework for exploring the significance of insights in interreligious dialogue processes. The study begins with an overview of representative perspectives on learning in interfaith dialogue and conflict transformation. Following this, I offer a detailed analysis of Lonergan's work on insight in understanding, judgment, and practical learning, highlighting the important role that insights play in structuring interpretation and communication in dialogue situations. Drawing on Lonergan's theoretical framework, I explore how insights are implicated in shaping communication in dialogues between religious actors, both in the development of conflicts, as well as in their transformation. Using case studies from dialogues involving Christians, Muslims, and Jews, I examine how mistaken insights can contribute to sustaining relationships of threat among parties in religious conflicts. I then examine how dialogue processes can act as catalysts for the emergence of new and more accurate insights that transform parties' understanding of the conflict. By helping parties correct mistaken interpretations and discover alternate ways of communicating, such insights can often play an important role in facilitating shifts from hostile patterns of interaction to more cooperative forms of engagement. Throughout, I explain how Lonergan's work offers significant advances over existing discussions of insight and its role in conflict transformation processes. His approach identifies a range of different types of insights, and thus facilitates an analysis of the different roles insights can play in structuring communication at different phases of dialogue processes. It also permits a more developed exploration of the various cognitional and environmental conditions that facilitate or frustrate the occurrence of insights in dialogue situations. His work thus constitutes an important resource for theorists and practitioners seeking a better understanding of the cognitive dynamics that contribute to the transformation of interreligious dialogue processes.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.038 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".