Where there is no local author: a network bibliometric analysis of authorship parasitism among research conducted in sub-Saharan Africa
Bibliographic record
Abstract
INTRODUCTION: Authorship parasitism (ie, no authors affiliated with the country in which the study took place) occurs frequently in research conducted in low-income and middle-income countries, despite published recommendations defining authorship criteria. The objective was to compare characteristics of articles exhibiting authorship parasitism in sub-Saharan Africa to articles with author representation from sub-Saharan African countries. METHODS: A bibliometric review of articles indexed in PubMed published from January 2014 through December 2018 reporting research conducted in sub-Saharan Africa was performed. Author affiliations were assigned to countries based on regular expression algorithms. Choropleth maps and network diagrams were created to determine where authorship parasitism occurred, and multivariable logistic regression was used to determine associated factors. RESULTS: Of 32 061 articles, 14.8% (n=4754) demonstrated authorship parasitism, which was most common among studies from Somalia (n=175/233, 75.1%) and Sao Tome and Principe (n=20/28, 71.4%). Authors affiliated with USA and UK institutions were most commonly involved in articles exhibiting authorship parasitism. Authorship parasitism was more common in articles: published in North American journals (adjusted OR (aOR) 1.26, 95% CI 1.07 to 1.50) than in sub-Saharan African journals, reporting work from multiple sub-Saharan African countries (aOR 8.41, 95% CI 7.30 to 9.68) compared with work from upper-middle income sub-Saharan African countries, with <5 authors (aOR 14.46, 95% CI 12.81 to 16.35) than >10 authors, and was less common in articles published in French (aOR 0.60, 95% CI 0.41 to 0.85) than English. CONCLUSIONS: Authorship parasitism was common in articles reporting research conducted in sub-Saharan Africa. There were reliable predictors of authorship parasitism. Investigators and institutions in high-income countries, as well as funding agencies and journals should promote research from sub-Saharan Africa, including its publication, in a collaborative and equitable manner.
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How this classification was reachedexpand
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 | 0.091 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.456 | 0.968 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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; both teacher heads agree on what is shown here.
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".