No ‘I’ in Anatomy: Group Cadaveric Dissection
Bibliographic record
Abstract
With the advent of new teaching technologies, gross anatomical education has come under heavy scrutiny. Much of the research focuses on quantitative outcomes; content‐related post‐tests are often the metric for comparison. However, there is a paucity of research surrounding the social nature of interpersonal interaction pertaining to learning around a cadaver. Using a case‐study approach, we will begin to explore the social nature of the cadaver laboratory. The roles filled by students around the dissection table are of special interest. Four random second‐year undergraduate Kinesiology lab groups (n=20) were observed over a nine‐week period during routine labs. Observations were made using recorded video and observational field notes. Participants were also invited to an interview to explore pertinent observations further. Through systematic analysis, we will identify and describe roles within the social context of the laboratory. It is hoped that a better understanding of the qualitative aspects of the laboratory might better inform curricular architecture in the future. Grant Funding Source : Queen Elizabeth Scholarship and Western Graduate Research Scholarship
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 | 0.015 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".