Bibliographic record
Abstract
The demanding, competitive, and output-centred culture of higher education often trickles into our teaching where focus is on the summative product rather than the process of learning. Reflection is one means of encouraging deeper and richer understandings during the learning process, and is a form of purposeful thinking that can be used to explore complex problems, anticipate outcomes, or be used on unstructured ideas to gain clarification (Larrivee & Cooper, 2006; Ryan, 2013). Reflection can be applied in higher education to enable a learner to grow intellectually, professionally and personally (Rogers, 2001; Ryan, 2011). The process of reflection allows the learner to seek out personal meanings and identifications with the learning material and create connections with the ideas and content already known (Ash & Clayton, 2009). This process fosters further learning, as the individual develops new concepts, relationships, and perspectives and also reinforces their current understandings through this feedback system (Rogers, 2001). This workshop engages participants in a pre-facilitation activity on reflective writing used to represent the reflection process and to further illustrate what reflection is, how it can be used as a learning skill, and how it can be assessed.
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 | 0.046 | 0.110 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.048 |
| Scholarly communication | 0.018 | 0.029 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.011 | 0.018 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".