Peer review report for: SME performance and job creation in Sub-Saharan Africa: The role of digital innovation
Bibliographic record
Abstract
PEER REVIEW REPORT FOR: Solaja, O. A., Oyedele. O. O., Olajugba, O. J., Abiodun, A. J., Edewor, O. J., Solaja, O. O., Kehinde, O. V and Akerelee, F. O. (2025). SME performance and job creation in Sub-Saharan Africa: The role of digital innovation. RAE-Revista de Administração de Empresas, 65(2), 2025. e2023-0553. http://dx.doi.org/10.1590/S0034-759020250201 HOW TO CITE THIS PEER REVIEW REPORT: Solaja, O. A., Oyedele. O. O., Olajugba, O. J., Abiodun, A. J., Edewor, O. J., Solaja, O. O., Kehinde, O. V and Akerelee, F. O. (2025). Peer review report for: SME performance and job creation in Sub-Saharan Africa: The role of digital innovation. RAE-Revista de Administração de Empresas, 65(2), 2025. e2023-0553. Zenodo. http://dx.doi.org/10.5281/zenodo.14238807 Disclaimer: The content of the Peer Review Report is the full copy of the reviewers' and authors' reports. Typing and punctuation errors are not edited. Reviewers: Fred Peter, https://orcid.org/0000-0003-2167-8446, Landmark University College of Business and Social Sciences, Omu Aran, Nigeria The second reviewer did not authorize disclosure of their identity. The third reviewer did not authorize disclosure of their identity and peer review report.
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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 | 0.024 | 0.176 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.022 | 0.010 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.428 | 0.456 |
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".