Fostering research integrity in sub-Saharan Africa: challenges, opportunities, and recommendations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.044 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.019 |
| 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 it