2010 NEWSMAKERS: Celebrating our own
Bibliographic record
Abstract
How will we remember 2010? Probably through these faces. This year, the Western News starts a new, if not wholly original, tradition. Our Newsmakers 2010 section celebrates the best of research, academia and volunteer spirit that we have to offer on this campus. We spotlight, in brief words and striking images, the accomplishments of some of our favourites from the last year. One of the most powerful women in Canada. A student with an eye for the greater good. And a man who would do anything, including posing with a zebra, for the United Way. A football coach. A ground-breaking wind researcher. A citizen soldier. A pioneering administrator. An Olympic expert. Labour leaders. Even a man who hasn't started work yet. Each contributed positively to important conversations on this campus. They are how we will remember 2010. Understand, we honour a mere handful of the hundreds who shaped the last year at The University of Western Ontario. Some of these names you'll know by heart. Others, you may need a little help to remember. But all were part of what makes this university community so grand.
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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.017 | 0.004 |
| Scholarly communication | 0.018 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.054 | 0.023 |
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".