Disparities in Global Authorship and Data Source in the <i>Pediatric Blood and Cancer</i> Journal 2011–2021: Realities and Strategies for Improvement
Bibliographic record
Abstract
BACKGROUND: Research expands knowledge and improves outcomes. Research is needed in all settings, but most often occurs in high-income countries (HIC) compared to low- and middle-income countries (LMICs). Publication in scientific peer-reviewed journals and authorship position are important for academic/clinical advancement. We explored the current state of global authorship and data source distribution for publications in the Pediatric Blood and Cancer (PBC) journal. PROCEDURE: LMIC-affiliated author inclusion and position in selected article categories of the PBC (2011-2021) were recorded. Articles with at least one LMIC-affiliated author (first-listed affiliation) and 5% of exclusively HIC-authored articles were verified. Descriptive statistical analysis was performed. RESULTS: Of 4504 articles reviewed, 593 (13%) included at least one LMIC-affiliated author (517/593 [87%] as first author and 488/593 [82%]) as senior author. In a subset of articles with LMIC-sourced data, 148/675 (22%) included exclusively HIC authors. Within the LMIC-sourced data subset, 81/675 (12%) articles were mixed HIC/LMIC affiliation and 446/675 (66%) were exclusively LMIC-affiliated. The frequency of LMIC-affiliated authors as first or senior author within HIC/LMIC-affiliated collaborations was 31/81 (38%) and 9/81 (11%), respectively. CONCLUSION: As more than 80% of children live in LMICs and the WHO Global Initiative for Childhood Cancer is increasingly engaged across LMICs, all researchers/clinicians must justly be given an opportunity to conduct, write, publish, and be recognized for their research. PBC is uniquely poised to promote equitable publishing practices and opportunities for professional recognition by drawing on emerging best practices for equitable authorship, including potentially restructuring authorship guidelines and requirements.
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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 | 0.083 | 0.236 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.026 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".