Bibliographic record
Abstract
Traditional university education has focused on academic learning, which is followed by a graduate’s attempts to apply this learning to various career-related pursuits. Experiential learning turns this focus on its head – at least partially. Instead of learning preceding praxis, learning now follows praxis. In this latter model, much of the post-praxis learning is focused and achieved via reflective analysis of experience – also called reflective practice – through written reflection. Reflective Practice Writing (RPW), also called Reflective Practice Journaling, is much more than traditional journaling. For reflective practice to effectively facilitate the learning process for students, RPW requires students to deeply probe and explore their experience to realize maximum learning. A guide or a “tool” to assist this process is useful and, I argue, required but, in too many cases, is either inadequate or not provided at all. This paper provides and describes such a reflective practice writing tool, which has been imagined as a bicycle – with a “front wheel” and a “back wheel” of spokes or questions. A reflective practice writing tool cannot, however, simply be developed on its own; it must be tied to a teaching and learning philosophy which has student learning integration and, ultimately, student transformation as its goal. The Reflective Practice Writing Bicycle is based precisely on such a teaching/learning philosophy, which is integrated into The RPW Bicycle tool itself.
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 | 0.023 | 0.092 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.021 | 0.025 |
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".