Change agent teaching for interreligious collaboration in Black Lives Matter times
Bibliographic record
Abstract
Abstract Interreligious discourse opens ways to discover how shared justice values and ethical actions can inform our responses amid the unjust disparities that privilege some and marginalize many. At present, we live in a public landscape of societal dissonance that heightens the urgency for people with theological grounding to bridge collaborative public witness among varied religious traditions. I describe “Black Lives Matter times” as a present expansive era in which the stew of public tension, stirred by decades of structural disparities, link religion and politics to an agenda to dismantle equitable public policies after the Civil Rights era. My goal for writingChange Agent Church in Black Lives Matter Times: Urgency for Action(2020) is to encourage practitioners and educators to prepare for public justice ministry that I callpublic witness. The book offers analytical discussion points for critical reflection and a toolkit of process methods to mobilize for public justice roles and strategic actions as collaborative change agents. In this article, as in the book,public witnessis described as “faith‐informed commitment to take action in solidarity with the marginalized, and to help mobilize change” (2). As the title conveys, the termchange agentsignifies “the ways in which clergy and interfaith leaders could exemplify a public justice ethic as motivational catalyst” (6). To prepare for “change agent teaching” shifts from teacher‐centered lectures to a learner‐centered focus to build core competencies for interactive relational engagement. Change agent teaching of religion and theology can connect faith and praxis, as theopraxis, to frame justice components of public witness. As well, interactive learning helps to develop competencies under two rubrics: contextualization and conscientization. (a)Contextualizationconnects experiential narratives to respect lived experiences at the intersections of our individual and collective identity. (b)Conscientizationraises awareness with inclusive scrutiny of issues to articulate how theological and ethical values ortheoethicsinfluence our actions. This article focuses on pedagogical approaches and process methods to teach and learn about the change agent role of public witness to support an ethos of restorative justice as understood across faith traditions.
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 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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".