Canada India Healthcare Summit 2021
Bibliographic record
Abstract
The Canada India Health-care Summit 2021, (“CIHS 2021”), is the 3 rd Summit, focusing on healthcare, organized by Canada India Foundation, as part of an ongoing series of thematic Canada India Forums, to highlight opportunities for collaboration between Canada and India in key strategic sectors and make public policy recommendations to the respective governments. The Federation of Indian Chambers of Commerce and Industry, Toronto Rehabilitation Institute – University Health Network and the Consulate General of India in Toronto were co-organizers of the Summit. CIHS 2021 was focused on three themes: (1) artificial intelligence and its contribution to overcome COVID-19, (2) biotechnology and its contribution to overcome COVID-19, and (3) pandemic responses and initiatives. The Summit was held on May 20, 2021– May 21, 2021, and was preceded by three webinars. More than 60 healthcare experts and government leaders spoke at the Summit, to nearly 500 virtual attendees. A full report of the Summit with specific policy recommendations was made to the Canadian and Indian governments.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.014 | 0.002 |
| Scholarly communication | 0.010 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.038 | 0.011 |
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".