Quick Responses of Canadian Social Scientists to COVID-19: A Case Study of the 2020 Federal COVID-19-Specific Grant Recipients
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it