Editorial: Cognitive Development in Informal Learning Institutions: Collaborations Advancing Research and Practice
Bibliographic record
Abstract
Researchers in cognitive development, and developmental science more broadly, are encouraged to 14 bring our science into the "messiness of the real world" (Golinkoff et al., 2017(Golinkoff et al., , p. 1407). Many have 15 heeded this call by establishing partnerships with community and educational institutions (e.g., 16 museums, science centres, libraries), and in some cases, engaging in studies the fruits of which have 17 the potential to serve real-world applications (Callanan, 2012;Haden, 2020;Sobel & Jipson, 2016). 18 The goal of this research topic was to spotlight the growing number of collaborations with informal 19 learning institutions and illustrate the cutting-edge cognitive and social cognitive research occurring 20 through these partnerships across a range of topics. 21 22 We thank all authors who submitted manuscripts and reviewers that provided thoughtful critiques.
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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 | 0.011 | 0.050 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.019 | 0.018 |
| Insufficient payload (model declined to judge) | 0.019 | 0.013 |
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".