Bibliographic record
Abstract
In 1822, French botanist Auguste de Saint-Hilaire warned, ‘Either Brazil annihilates the leaf-cutting ants, or the leaf-cutting ants will annihilate Brazil’. Two centuries later, his remark about how small creatures could challenge such a vast country resonates anew, this time with mosquitoes. In ‘Viruses and Reproductive Injustice: Zika in Brazil’, the French historian of medicine, Illana Löwy, unpacks how the tiny Aedes aegypti mosquito has exposed the fragility of public health systems and the profound inequalities woven into Brazil’s social fabric. Löwy’s account highlights that the 2015 Zika outbreak in northeastern Brazil was not merely a public health crisis; it also revealed deep-seated social inequalities within the country. The most affected were poor, rural, non-White women who faced inadequate healthcare and limited reproductive rights. Contrary to earlier assumptions, researchers later found that the Zika virus, initially seen as harmless, was linked to a significant congenital disorder known as the ‘microcephaly epidemic’. Further epidemiological studies confirmed that infants born to mothers who contracted the virus during pregnancy typically had a head circumference of 32 cm or smaller, indicating congenital Zika syndrome (CZS), which could lead to potential neurological and cognitive disabilities. This situation created considerable challenges for the mothers known as Mães de Micro (‘micros mothers’). These women already faced heightened vulnerabilities due to their socio-economic conditions, experiencing increased exposure to mosquitoes and encountering barriers to effective contraception and reliable prenatal care. Moreover, they were excluded from the decriminalisation of abortion in cases of foetal abnormalities.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".