Bibliographic record
Abstract
Harvard chemist Alán Aspuru-Guzik has spent nearly half his life—20 years—in the U.S. He is sad to leave but feels he must. “The political dialogue is broken,” says Aspuru-Guzik, who was born in the U.S. and raised in Mexico. “People are not talking to each other; they are shouting at each other. I don’t want my kids to grow up in that kind of country.” So when Canada came knocking with a Canada 150 Research Chair at the University of Toronto, he accepted: “It looked like Canada could be a good home,” he says. “It’s a very inclusive society and has a commitment to funding basic science.” Aspuru-Guzik is one of 25 international researchers to be granted funding through the Canada 150 Research Chairs Program, which was announced as part of the country’s 150th anniversary celebrations last year. Federal Minister of Science Kirsty Duncan called the program a “brain gain”
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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.006 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.030 | 0.003 |
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".