A Postcolonial Conversational Approach to Preaching in Multicultural Contexts
Bibliographic record
Abstract
Preachers cannot assume the mere presence of different cultures or diversity means a congregational context is multicultural. Fostering an environment conducive to multiculturalism can be difficult, partly due to the persisting colonial structures. The colonial systems created spaces where different cultures and diverse groups interacted, yet these interactions were destructive. The goal of integrating, especially understood through assimilation, cultures into the existing system limits multiculturalism. This article outlines three inter-related foci for preaching, especially preaching where both the preacher and the congregation have social privilege, to foster healthy multiculturalism. Drawing from the works of Jared Alcántara and Matthew Kim, I recognize the need for preachers and congregations to increase their intercultural competence and hermeneutical tools for recognizing, interpreting, and ethically navigating biblical and modern cultures. Because some preachers and congregations have taken their cultural formation for granted, intercultural development is a critical step toward preaching in multicultural contexts. The article discusses Homi Bhabha’s The Location of Culture as the second major movement. His notions of hybridity and the distinction between diversity and difference are particularly helpful for pushing against colonial limits. Preaching in multicultural contexts needs to be approached as more than the sum of diverse cultures present and absent. Through the work of Bhabha, I conceive of preaching in multicultural contexts as fostering interstitial spaces which embrace difference, while resisting the objectification of culture. Turning more directly to the homiletical theory in the final section, I argue that O. Wesley Allen’s conversational model, guided by the concepts of interstitiality and hybridity, can develop preaching in multicultural spaces by emphasizing open-ended relational discovery rather than singular objective understanding. This conversational approach actively seeks relational participation where individuals are committed to mutual growth through critical interactions which account for culture as a general concept and particular cultures. This conversational reframing invites growth through multicultural understanding.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".