A Homiletic of Humour: The Use of Humour as Critical Method in Hermeneutic, Theological, and Preaching Practices
Bibliographic record
Abstract
This dissertation examines the strategic role of humour in the Synoptic Gospels and its implications for prophetic preaching. Drawing from a cross-disciplinary foundation that incorporates linguistics, feminist philosophy, critical humour theory, and biblical studies, this work seeks to address a notable deficiency within the existing scholarly literature on preaching, which has historically marginalized the role of humour in scriptural contexts. The analysis reveals that humour, far from being a mere rhetorical flourish, serves as a critical tool for engaging with theological themes, challenging oppressive structures, and fostering community cohesion. A key contribution of this study is the development of an analytical algorithm to identify the presence and role of humour within the Gospels. Within theological inquiry, the capacity of humor to encompass both tragedy and joy without succumbing to polarization is explored. The resulting theology of humour offers a way to build resilience and authenticity. Finally, the dissertation proposes a new homiletic framework that centres on humour, offering a significant contribution to exegetical and homiletic scholarship. This framework underscores the importance of humour in interpreting the Gospels, suggesting that it can provide new insights for engaging, relevant, and meaningful prophetic preaching.
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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 | 0.019 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.010 | 0.079 |
| Scholarly communication | 0.014 | 0.012 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| 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".