Stewarding Online Space in Making Disciples of Gen-Z
Bibliographic record
Abstract
The western church is experiencing a steady decline in membership with each passing generation. If Jesus is the only way, truth, and life available, then seeing fewer people come into life with Him should be very alarming for the Church today, and a major concern to address. How can the Church re-engage youth in today’s post-Christian culture? As Gen-Zs are digital natives with much of their experiences being lived out online, what role should the online environment play in the Church in reengaging today's generation of young people? How can churches effectively disciple young people they may never meet in person? What answers might the field of education have to offer practical ministry in these questions? The purpose of this literature review is to examine the available scholarship related to online discipleship and the Gen-Z generation, including how faith nurture, church, community development, learning, and online collaborative learning theory can be understood within the online context. It will also look at the model of traditional discipleship and examine whether the online world is an appropriate place for such discipleship practices. Finally, this paper will offer suggestions based on the literature as to how one may leverage online collaborative learning practices to engage effectively in online discipleship with Gen-Z individuals in a small group setting.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".