Canadian social science workforce in COVID-19 rapid research
Bibliographic record
Abstract
This dataset includes the social science researchers’ information from the COVID-19 rapid response research projects funded by two Canadian Federal government funding agencies, namely the Social Sciences and Humanities Research Council (SSHRC) and the Canadian Institutes of Health Research (CIHR). SSHRC and CIHR are the two major funding agencies in Canada, especially for researchers affiliated with universities and research institutes across Canada. The COVID-19 rapid response research opportunities were considered the first nationwide quick response disaster research in Canada’s history. This data presents information on researchers who were awarded funding from March 2020-April 2021 on COVID-19 related grants. SSHRC grants included: Partnership Engage Grants COVID-19 Special Initiative: September 2020 Competition and June 2020 competition. CIHR grants included: Operating Grants: Strengthening Pandemic Preparedness in Long-Term Care (COVID-19), COVID-19 Mental Health & Substance Use Service Needs and Delivery, COVID-19 May 2020 Rapid Research Funding Opportunity, Knowledge Synthesis: COVID-19 in Mental Health and Substance Use, and Canadian International COVID-19 Surveillance Border Study and Canadian Immunization Research Network: COVID-19 Vaccine Readiness Funding Opportunity. The data includes the research title, researchers’ project roles, contact information (affiliations, geographic locations, education level, and professional websites) and disciplines. As Canada has two official languages, both English and French projects are included. This dataset portrays the landscape of COVID-19-specific hazards and disaster research workforce in the Canadian social sciences community. Researchers from Canada and internationally could use this dataset to identify their potential research partners in Canada and collaboratively develop research partnerships for post-COVID-19 research in particular, as well as hazards and disaster research in general. The general public could use this dataset to contact their researchers within their communities to request related knowledge and skills. Prospective students could utilize this dataset to find related educational organizations, programs, and supervisors to pursue higher-level education.
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 | 0.006 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.014 | 0.041 |
| Science and technology studies | 0.008 | 0.001 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.136 | 0.039 |
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".