Bibliographic record
Abstract
Benjamin Prud’homme is the Vice-President, Policy, Society and Global Affairs at Mila – Quebec Artificial Intelligence Institute, one of the largest academic communities dedicated to AI. At the time this text was written, he was the Executive Director of the AI for Humanity department at Mila. Previously, he was a litigator in human rights, constitutional, and family law until his 2018 appointment as policy advisor to the Minister of Justice of Canada. In 2019, he became the advisor to the Minister of Foreign Affairs of Canada on matters of human rights and multilateral relations, a position he occupied until joining Mila. His work and publications focus on the role of human rights in the international governance of AI, the inclusion of marginalized individuals and communities in the life cycle of AI systems, and the epistemology of interdisciplinarity. An expert for the United Nations Broadband Commission’s Working Group on AI Capacity Building, he sits on the Advisory Board of Sustainability in the Digital Age (a think tank) and the Board of Directors of the Canadian Civil Liberties Association.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.147 | 0.015 |
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; both teacher heads agree on what is shown here.
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".