Fostering research integrity in sub-Saharan Africa: challenges, opportunities, and recommendations
Bibliographic record
Abstract
Integrity and adherence to appropriate ethical standards are important elements of research. These standards are key to protecting research participants´ rights as well as ensuring the reliability and quality of research outputs. Although empirical evidence is scanty, several authors have alluded to the fact that violation of research integrity standards could be common in low- and middle-income countries including sub-Saharan Africa (SSA). Understanding the issues, challenges, and opportunities of research integrity and ethics in SSA is key to promoting the responsible conduct of research and the protection of research participants. This paper presents the authors´ critical views and recommendations on the current state of research integrity in SSA. We argue that understanding the current research integrity architecture in SSA has the potential to identify opportunities to promote responsible conduct of research in SSA. Such opportunities include, but are not limited to transparency, accountability, and reproducibility of research, which collectively lead to enhanced public trust in the research enterprise. We highlight the need to embrace equity, fairness, diversity, and inclusivity in the research cycle from conception (priority setting), funding, implementation, dissemination of findings, and scale up. We move on to provide a rationale for understanding the differences and similarities between research ethics and research integrity. Governments, research, and academic institutions must develop multifaceted approaches to promote compliance with principles of research integrity by developing and implementing clear research integrity policies and guidelines that foster responsible conduct of research and prioritize capacity building and empowerment of early career researchers, students, and other targeted key stakeholders.
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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 | 0.147 | 0.216 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.009 | 0.026 |
| Scholarly communication | 0.027 | 0.041 |
| Open science | 0.006 | 0.016 |
| Research integrity | 0.018 | 0.025 |
| Insufficient payload (model declined to judge) | 0.007 | 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; 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".