Bibliographic record
Abstract
This thesis project argues that we ought to ensure a global equitable distribution of vaccines (GEDV) during a pandemic, using lessons learned from the Covid-19 pandemic.In my first chapter I argue that wealthy nations are the ones most responsible for ensuring GEDV based on their capacity and self-interest in the matter.In my second chapter I explain that wealthy nations stockpiled vaccines for themselves because of their nationalist values that prioritize the health and safety of their own citizens.In my final chapter I propose a hypothesis that we ought to try something different next time a pandemic happens, and I offer a possible solution which I call the efficient altruism principle, which states that wealthy nations ought to ensure GEDV because doing so is within their own self-interest.Achieving herd immunity through vaccination will end the pandemic sooner and, therefore, save more lives and money overall.10 I elaborate on this at greater length in my third chapter.11 CDC, "COVID-19 and Your Health," Centers for Disease Control and Prevention, February 11, 2020, https://www.cdc.gov/coronavirus
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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 | 0.010 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.008 | 0.017 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".