Quick Responses of Canadian Social Scientists to COVID-19: A Case Study of the 2020 Federal COVID-19-Specific Grant Recipients
Bibliographic record
Abstract
Abstract COVID-19 prompted an abundance of independent and collaborative quick response disaster research (QRDR) initiatives globally. The 2020 federal COVID-19-driven granting opportunities initiated the first official QRDR effort in Canadian history, engaging social scientists to rapidly address the pandemic-related societal influences. This study aims to portray the landscape of this nascent social science QRDR workforce through the first round of federal COVID-19-specific grant recipients. A case study approach was employed to analyze 337 social science projects with 1119 associated researchers, examining the demographic structure of these COVID-19-driven social science researchers and their research projects’ characteristics. Accordingly, the findings are presented through the following two streams: (1) From a researcher perspective, this case study describes researcher typology, geographic location, primary discipline, and educational background, highlighting the diverse characteristics of social sciences researchers, and uneven research development across Canada. (2) From a research project perspective, this case study identifies and synthesizes research project subjects, themes, collaborations, and Canadian distinctions, emphasizing the need for galvanizing cooperation and focusing on uniquely Canadian contexts. The case study illustrates challenges associated with data curation that pose barriers to developing a nuanced understanding of the Canadian social science community COVID-19 research landscape. Consequently, the case study develops three recommendations to improve QRDR development in Canada: promoting information transparency, dissemination, and updates; improving hazards and disaster research workforce evaluation; and enhancing multi-stakeholder cooperation.
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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 | 0.018 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.086 | 0.017 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".