From Artificial Intelligence Bias to Inequality in the Time of COVID-19
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
As secretary general of the United Nations, Antonio Guterres said during the 2020 Nelson Mandela Annual Lecture, “COVID-19 has been likened to an X-ray, revealing fractures in the fragile skeleton of the societies we have built.” Without a doubt, the COVID-19 pandemic has exposed and exacerbated existing global inequalities. Whether at the local, national, or international scale, the gap between the privileged and the vulnerable is growing wider, resulting in a broad increase in inequality across all dimensions of society. The disease has strained health systems, social support programs, and the economy as a whole, drawing an ever-widening distinction between those with access to treatment, services, and job opportunities and those without. Global lockdown restrictions have led to increases in childcare and housework responsibilities, and most of the burden has fallen on women, further increasing existing gender inequality [1], [2]. Indigenous populations worldwide find themselves more vulnerable to infection, many times with less access to health services or hygiene measures and limited updated scientific information about the virus and measures that can be taken to mitigate it [3]. Inequality has also pervaded the education sector, with only a subset of students able to attend safe in-person schooling or access online education when needed.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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