Publishing child development research from around the world: An unfair playing field resulting in most of the world's child population under‐represented in research
Bibliographic record
Abstract
Abstract It has become increasingly apparent that publishing research on child development from certain countries is especially challenging. These countries have been referred to collectively as the Majority World, the Global South, non‐WEIRD (Western, Educated, Industrial, Rich, and Democratic), or low‐ and middle‐income countries. The aim of this paper is to draw attention to these persistent challenges, and provide constructive recommendations to contribute to better representation of children from these countries in child development research. In this paper, we outline the history of publication bias in developmental science, and issues of generalization of research from these countries and hence where it ‘fits’ in terms of publishing. The importance of explaining context is highlighted, including for research on measurement child development outcomes, and attention is drawn to the vicious publication‐funding cycle that further exacerbates the challenges of publishing this research. Specific recommendations are made to assist child development journals achieve their stated goals of creating a more inclusive, equitable, diverse, and global field of child development.
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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 | 0.205 | 0.398 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.014 |
| Science and technology studies | 0.007 | 0.017 |
| Scholarly communication | 0.031 | 0.016 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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; the direct Gemma label and the distilled Codex classifier 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".