Bibliographic record
Abstract
The COVID-19 pandemic presented an array of challenges, characterized by its unpredictability and rapidly changing nature. While often referred to as an "unprecedented" crisis, it is important to recognize the shared traits it possesses with previous pandemics. To fortify our society against future outbreaks, continued investment in pandemic preparedness is essential. This paper aims to extract valuable insights from four crucial sectors within Canada's healthcare system during the COVID-19 pandemic. The identified areas of focus include: 1) Addressing the "disinfodemic" by effectively countering the dissemination of misinformation; 2) Promoting justice in resource allocation to ensure fairness and equity; 3) Strengthening healthcare infrastructure to meet the demands of resource-intensive crises like COVID-19; and 4) Mitigating the long-term impact of stringent pandemic-related restrictions. Through an honest reflection on these key sectors, this paper sheds light on crucial lessons learned and paves the way for robust preparedness strategies for future outbreaks.
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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.021 | 0.028 |
| Scholarly communication | 0.017 | 0.010 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.004 | 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".