Bibliographic record
Abstract
In the twenty-first century, the Bible is more accessible to North American Christians than ever before, yet a problem exists, in that youth are struggling to read it and understand how it applies to their life. As a youth worker with an organization called “Young Life,” I routinely witness Generation Z’s challenges in engaging with Scripture. This is concerning, as Young Life places a high value on teaching the Bible to adolescents and is an organization dedicated to mentoring and discipling Christian teenagers (called “Campaigners”) as they grow into resilient and missional followers of Jesus. In this practical theology dissertation, I focus on one element related to youth discipleship: the practice of Bible teaching. Through working with Young Life, I have seen the important role of teaching Scripture in teenagers’ faith formation. As such, I conducted this qualitative research study to learn how my colleagues in Young Life instruct the Bible to Campaigners. My intention in doing this is to develop a pedagogical theory to present to Young Life about how to conceptualize Bible studies and effectively teach Scripture to students. More specifically, the primary research question I address in this dissertation is: What pedagogical strategies would help better train Campaigners to read and engage with the Bible as they grow into resilient disciples? To address and answer this primary question, I ask three secondary questions: (1) What current methods of teaching the Bible do Young Life staff use?; (2) Which of these methods are effective or ineffective and why?; and (3) What are effective pedagogical techniques to assist adolescents in learning? In theologically reflecting on my findings, I argue that method should not be the only consideration when teaching Scripture. For Bible studies to connect well with Campaigners, I hypothesize that they must be rooted in a framework of teaching that takes seriously students’ worldviews. By drawing attention to the role of epistemology vis-à-vis method, this project provides an extensive layer of depth to the theoretical and theological foundation that pedagogy is built upon. I do this by developing a concept I call “worldview apologetics,” which is a way of teaching the Bible that takes seriously the cultural lenses through which teenagers see and interpret the world.
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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 | 0.010 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.015 | 0.026 |
| Scholarly communication | 0.020 | 0.014 |
| Open science | 0.003 | 0.021 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".