Advancing Transparency and Openness in Child Development Research: Opportunities
Bibliographic record
Abstract
Transparency and openness are basic scientific values. They are at the heart of practices that accelerate discovery and broaden access to scientific knowledge. We make the case that transparency and openness are essential values and principles for the enduring influence of child development research and for SRCD’s ability to deliver on, sustain, and nurture its mission for the benefit of diverse global stakeholders and constituents. A companion paper (Gilmore et al., 2019) discusses the challenges with realizing SRCD's vision for a science of child development that is open, transparent, robust, impactful, and conducted with the highest standards of integrity. Here, we discuss the opportunities and ways in which the society can set standards and recommendations to ensure the full integration of such transparency and openness for the future of developmental science.
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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 | 0.218 | 0.249 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.009 | 0.048 |
| Scholarly communication | 0.028 | 0.061 |
| Open science | 0.004 | 0.044 |
| Research integrity | 0.017 | 0.022 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".