Reimagining character formation in the Christian university in challenging times
Bibliographic record
Abstract
Changes in the demographic profile of students attending Christian universities combine with shifts in the culture at large to present new challenges to Christian higher educators who have the character formation of students as an aim. The pandemic will bring other challenges. In uncertain times, Christian universities aiming at character formation must, first, clarify and focus on their mission and must, second, work intentionally to create a campus climate supportive of character development. A Christian university wanting a climate that fosters character makes these seven efforts (among others): to build relationships and community, to build trust, to welcome dialogue on difficult issues, to consider the built environment, to go slow, to recognize the diversity of learners, to attend to its language. Uncertain times and their concomitant challenges present new opportunities for Christian universities to reimagine character formation.
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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.027 | 0.028 |
| Scholarly communication | 0.018 | 0.009 |
| Open science | 0.001 | 0.022 |
| Research integrity | 0.002 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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".