Review of: "Winner-takes-all Majoritarian System and Irregularities in Six Election Cycles in Nigeria, 1999 – 2019"
Bibliographic record
Abstract
This research addresses an interesting issue that could contribute to the strengthening of knowledge of democracy studies in Africa and the world.The focus on political violence as an integral strategy of the electoral process is very interesting, however, this aspect seems to be under-exploited in the article.The article would benefit from major modifications before publication.In particular, it would need to be reorganized, some parts need to be synthesized and the literature review, analytical framework and methodology need to be strengthened.The nature of the data analyzed should be directly announced in the introduction and abstract, as well as the method of data collection and the reasons why these data are relevant and what their limitations are.It would also be important to add to the literature review 1) theoretical approaches to political regimes and democratic studies and 2) the presentation of studies already done on these aspects in Nigeria or in other comparable case studies.For example, references to Fjelde and Höglund may be too frequent: it would be needed to diversify the sources used.It would also be important to strengthen the analytical framework and better explain how it helps to analyze the data mobilized.For the reorganization of the article, the first two sections could be largely synthesized and enriched with a better contextualization of the state of knowledge of the phenomena analyzed in Nigeria, as well as a strengthened and better highlighted analytical framework.Finally, you could also tighten and synthesize the presentation of your data to give more space to the discussion and analysis, which are for the moment not very perceptible and do not really answer the research question.
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.
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 | 0.010 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".