An introduction to “Sugar and spice, and everything nice: exploring prosocial development through infancy and early childhoodâ€
Bibliographic record
Abstract
Paulus, 2014).For example, helping behaviors are demonstrated by infants as young as 18 months of age (e.g., Warneken and Tomasello, 2006) and sharing behavior begins to emerge at around 2 years of age (e.g., Rheingold et al., 1976).Such prosociality is essential to social functioning in many respects.However, while prosocial behavior has long been of interest to developmental researchers, there remains much we do not know about the early development of prosocial behaviors.This research topic builds on a well-established area of research, and brings together the work of various researchers in the field of prosocial development who have contributed unique theoretical perspectives, insightful reviews, and novel empirical work.The goal of this research topic is to examine broadly how, why, and when a spectrum of behaviors emerge, and enhance our understanding of the beginnings of human prosociality.Here, the existing literature is reviewed, and new insights into the development of prosocial behaviors are offered.A broad range of topics such as helping, cooperation, sharing, inequality aversion, and moral reasoning are covered, and various factors influencing prosociality explored.As discussed in a theoretical contribution by Keith Jensen, Amrisha Vaish, and Marco Schmidt, prosociality is unique to humans and its development is influenced by a variety of mechanisms such as empathy, other-regarding concerns, and normativity.Importantly, the term prosocial behavior encompasses a multitude of behaviors, however, in her review article, Kristen Dunfield proposes that other-oriented, prosocial actions can be categorized into three specific subtypes; sharing, helping, and comforting, drawing on existing literature to support her proposal.These subtypes of prosocial behaviors are further explored in this research topic using a variety of approaches.For instance, helping in early childhood is explored in a theoretical contribution by Stuart Hammond.While Hammond discusses the development of early helping behavior using a Piagetian framework, helping is also explored empirically by Sunae Kim, Beate Sodian, and Markus Paulus, who investigate differences in children's expectations regarding instrumental helping and self-helping at different points in 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.032 | 0.012 |
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".