"All in this together”: the global duty to contribute towards combating the Covid-19 pandemic
Bibliographic record
Abstract
This paper explores the unique realities and effects of Covid-19 as experienced in the global North and global South with special reference to Canada and sub-Saharan Africa; it also examines the moral responsibilities countries have towards their own people and the duty they have to work together to minimise and mitigate the devastating effects of the pandemic worldwide. We illuminate the importance of countries sharing their own world views, strengths, and expertise, and learning from one another in order to better situate all in tackling the pandemic. We argue that it is only insofar as all countries work collaboratively commensurate to each party's capacity to contribute towards the tackling of the Covid-19 pandemic that we may truly be said to be "all in this together". Keywords; Covid-19, global North, global South, solidarity, sub-Saharan Africa, global health</em></div>.
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How this classification was reachedexpand
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 | 0.026 | 0.127 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".