SHEPHERDS OF THE STEPPES: THE EXPERIENCE OF MALE EVANGELICAL MONGOLIAN CHURCH LEADERS, AN ETHNOGRAPHIC APPROACH. By Mark D.Wood. American Society of Missiology Monograph Series, 64. Eugene, OR: Pickwick Publications, 2023. Pp. xvii +280. Paperback, $40.00.
Bibliographic record
Abstract
Wood explores the conversion, discipleship, cultural negotiation, and theological education and leadership development experiences of male evangelical church leaders in Mongolia. This study contributes to the growing body of Christian anthropology, to the almost nonexistent field of Mongolian Christian anthropology, and to the field of contextualized theological education. This book is useful for anyone interested in Christianity in Mongolia, cultural negotiation, and cross-cultural theological education. After the introductory chapters that cover the literature review and explain his methods and procedures, Wood shares insights from interviews with over thirty leaders. He identified several factors contributing to the leaders' conversions and described their discipleship experiences. While some of these factors were tied to a unique time in history (the collapse of socialism in Mongolia), the connected emotions are more universal and give current workers insight into indicators of Mongolian receptivity to the Gospel. Woods explored four situations that require cultural negotiation for Mongolian believers: funerals, hair-cutting ceremonies, use of the khada (Buddhist prayer and blessing scarf), and celebrating Tsagaan Sar. Further, he showed how the Mongolian church leaders navigate these negotiations in different ways (and for different reasons). Next, he illustrated that while opportunities for theological training abound, there is a desire for training that is more targeted toward the pastors’ actual and felt needs and delivered in culturally contextualized ways. The insights from the theological education and leadership development chapter would be interesting to anyone engaged in these tasks in a cross-cultural context, especially for those who might feel the tension between how a culture might want content delivered and how it might be most effectively delivered. The study was limited to males, which is its major weakness. Without data from female leaders' experiences to compare it to, we do not learn anything unique about the male experience, and we lose out on female voices.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".