PhD candidates Okechukwu (Jake) Effoduh and Inbar Peled (along with three more York grad students) named Vanier Scholars for 2019
Bibliographic record
Abstract
Five PhD students from York University have been named Vanier Scholars and will receive support from the Government of Canada to pursue their cutting-edge research.\nValued at $50,000 per year for three years during doctoral studies, the 2019 Vanier Graduate Scholarship is awarded to graduate students who demonstrate leadership skills and a high standard of scholarly achievement in the social sciences and/or humanities, natural sciences and/or engineering, and health. Candidates are weighted on three criteria: academic excellence, research potential and leadership.\nFrom India to Israel to West Africa, this year’s Vanier Scholars are pursuing research with enormous reach.\n“This year’s Vanier Scholars show once again that York students are at the forefront of their fields, producing work with global impact,” said Thomas Loebel, dean of the Faculty of Graduate Studies. “Year after year, they reaffirm that our graduate programs are where transformative knowledge is being made.”
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.183 | 0.061 |
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".