Bibliographic record
Abstract
The use of images in education is expanding, but clear and comprehensive guidelines on how to carry out visual activities with students of a variety of fields are difficult to find. With the case studies from Finland, Canada, the United Kingdom, Australia, Japan, Poland, Turkey and the United States, contributors to this volume offer detailed reflections on the pedagogical role of using images in higher education. Examples include drawing, collage making, video production, object-based learning, photography projects, and many more. The book constructs a solid argument for the further development of visual pedagogies in higher education, highlighting the need to support students in advancing their visual competency as it has become fundamental to command in everyday life and professional contexts. Contributors are: Gyuzel Gadelshina, Tad Hara, Joanna Kędra, McKenzie Lloyd-Smith, Gary McLeod, Olivia Meehan, Marianna Michałowska, Iryna Molodecky, Pınar Nuhoğlu Kibar, Paul Richter, Karen F. Tardrew, Rob Wilson and Rasa Žakevičiūtė.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.018 |
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; both teacher heads agree on what is shown here.
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".