The cross-cultural reflective model for post-sojourn debriefing
Bibliographic record
Abstract
Reflective writing is a practice often encouraged in study abroad programs. Reflection can be facilitated through experiential learning, but little research is available on how to guide or structure-related learning activities. In this article, we discuss the Cross-cultural Reflection model (CCR), which emerged through our own process of researching three commonly used models for reflective writing. We document our procedure for researching, creating, testing, and modifying the CCR model, before and after using it with students in a post-sojourn debriefing workshop. In the discussion, we examine which aspects of the models examined informed the CCR model and which elements we introduced as a result of working with the models in two research retreats. The sharing of the process is intended to inform practices of reflective writing in post-sojourn debriefing to enhance international experiences, programmes, and practices.
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 | 0.173 | 0.208 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.007 | 0.021 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 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".