Emerging complexities and rising omission: Contrasts among socio-ecological contexts of infectious diseases, research and policy in Brazil
Bibliographic record
Abstract
In this article, we explore elements that highlight the interdependent nature of demands for knowledge production and decision-making related to the appearance of emerging diseases. To this end, we refer to scientific production and current contextual evidence to verify situations mainly related to the Brazilian Amazon, which suffers systematic disturbances and is characterized as a possible source of pathogenic microorganisms. With the acceleration of the Anthropocene's environmental changes, socio-ecological instabilities and the possibility of the emergence of infectious diseases merge into a background of a ´twin insurgency´. Furthermore, there is a tendency to impose economic hegemony in the current Brazilian context, corroborating discourses and pressures to a scientific simplification and denial. With this, we assert that developmental sectoral actions and monoculture of knowledge characterize an agenda of omission, that is, a process of decision making that indirectly reinforces ecological degradation and carelessness in the face of the possibility of the emergence and spreading of new diseases, such as COVID-19. Tackling the socio-ecological complexity inherent in the risk of the emergence of infectious diseases requires robust co-construction of scientific knowledge, eco-social approaches, and corresponding governance and sophisticated decision-making arrangements.
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 | 0.015 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.009 | 0.025 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".