Promoting diversity and overcoming publication barriers in Latin American neuroscience and Alzheimer's disease research: A call to action
Bibliographic record
Abstract
Alzheimer's disease (AD) is a global health issue. Because AD is a condition demanding effective management, its socioeconomic burden is immense and threatens the health systems of both low- and middle-income (LMIC) and high-income (HIC) countries. However, while most of the HICs are increasing their budget for AD research, the situation is different in LMICs, and resources are scarce. In addition, LMIC researchers face significant barriers to publishing in international peer reviewed journals, including funding constraints; language barriers; and in many cases, high article processing charges. In this perspective, we discuss these disparities and propose some actions that could help promote diversity, and ultimately translate into improved AD research capacity in LMICs, especially in Latin American and Caribbean countries. HIGHLIGHTS: Researchers in low- and middle-income countries (LMIC) face increasing difficulties such as financial constraints, language barriers, and article processing charges.Publication fees, in particular, can be a significant barrier in the process of publication and equal access to scientific information.Publication fee equalization initiatives by publishing companies could reduce the scientific inequality that disadvantages researchers in LMICs.
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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 | 0.199 | 0.301 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.013 | 0.020 |
| Scholarly communication | 0.034 | 0.035 |
| Open science | 0.007 | 0.025 |
| Research integrity | 0.018 | 0.021 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".