Building Meaningful Relationships for Equity in the Publishing Ecosystem: Empowering Latin American Research Through Engagement
Bibliographic record
Abstract
Equity in scientific publishing requires removing financial barriers, structural transformation, and inclusive practices that empower researchers from historically marginalized regions. Here, we reflect on recent Wiley's initiatives supporting Brazilian researchers to integrate into the international publishing ecosystem, including discounted rates for open-access article processing charges, the Wiley-CAPES transformative agreement, and in-country capacity-building events. While some challenges persist, such as linguistic barriers and funding access, we underscore the importance of meaningful local engagement and the coordinated actions among publishers and funding agencies that are supporting a more equitable publishing ecosystem.
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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 | 0.019 | 0.063 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.015 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.013 | 0.018 |
| Insufficient payload (model declined to judge) | 0.007 | 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".