The Benefits of International Student Perspectives in a Global Cancer Workshop
Bibliographic record
Abstract
A recent opportunity to facilitate a faculty and student workshop in Global Health and Cancer Prevention allowed us, an international group of graduate students, to reflect on cancer disparities in our home countries as well as our understanding of these differences. This included discussing the complexity of achieving equitable cancer prevention globally, and learning from our shared and disparate experiences of public health systems across the world. It became clear through this process that being an international student, and working with other international students with distinct backgrounds, can result in an enriched learning environment. Our discussions highlighted gaps in our knowledge regarding other cultures, and gave each of us a new perspective on aspects of our own cultures, related to cancer, cancer- related risk factors, and more broadly. This forum demonstrated to us the benefits of the diversity international students can bring to the learning space.
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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 | 0.019 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.007 |
| Scholarly communication | 0.019 | 0.010 |
| Open science | 0.002 | 0.036 |
| Research integrity | 0.006 | 0.019 |
| Insufficient payload (model declined to judge) | 0.020 | 0.004 |
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".