Bibliographic record
Abstract
8 graduate student/recent graduate presentations on varying topics related to public health. Moderated by Dr. Jessica Prodger. Reporting of panel done by current GHS students of the 2021 class. Abstracts can be found under "Africa-Western Collaborations Day 2020 Abstracts". Presenters as follows:\nRoger Antabe et al., "HIV Prevention Among Heterosexual Blackmen in Ontario: The Need to Revisit Provincial Policy"\nAyah Karra-Aly, Adaku Ohuruogu, Georgia Raithby, Jasandeep Sehra, "The Power of Poop"\nRyan LaPenna et al., "Using a One Health Approach to Address the Challenges Posed by Rabies to Animals and People in Rurals Areas in Victoria Falls, Zimbabwe"\nGurleen Saini, Anusheh Khan, Priscilla Matthews, "Malaria Elimination"\nZhongtian (Eric) Shao et al., "Effect of Physical Maturation and Sexual Debut on HIV Susceptibility in Adolescent Males in Rakai, Uganda"\nSteven Trothen et al., "Evaluation of Cytokine Profiles within the Endocervical Tract of HIV-1 Infected Females"\nJason Were et al., "The Epidemiology of Overweight and Obesity in Ghana: Examination of Predictors and Risk Groups among Women of Childbearing Age"\nBianca Ziegler et al., "Antenatal Care Utilization in the Fragile and Conflict-Affected Context of the Democratic Republic of Congo"
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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.496 | 0.319 |
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".