Bibliographic record
Abstract
The past years have resulted in an increased use of technology in anatomy and medical education. The use of augmented and virtual reality, collectively often referred to as extended reality (xR) is moving from an experimental idea to a curricular reality. The confluence of a global pandemic and increased accessibility of emerging technologies has resulted in many xR endeavours and education applications. As we explore the use of these technologies, as we integrate them into our classroom, as we develop applications in these technologies, we need to ask the question about how we balance the technological affordances with our values grounded in ethics and inclusivity. Technology can change how we see the world and it can influence our affective response to education. In anatomy education, when using xR, we need to follow universal design principles in order to make the technology accessible to students of all abilities. An emphasis on the learner experience and intuitive interfaces makes the technology fade into the background and puts the academic content into the center of the learning experience. Deliberate choices of how anatomy is placed in the virtual space and whose anatomy we are visualizing grounds these approaches in an ethical framework. We are at the cusp of a new era in technology use, it is an opportunity to make sure that our way forward will reflect our values and build a compassionate, inclusive, and exciting approach to anatomy education.
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 | 0.001 | 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".