“There Must Be a Cat Nearby”: Kindergarteners’ Reasoning About Action at an Attentional Distance
Bibliographic record
Abstract
Action at a distance describes causal relationships in which causes and effects act at a distance. Many concepts in life and in science involve action at a distance, such as a remote control activating a television or magnets repelling each other without touching. Some forms occur within the same attentional frame, such as two magnets on a table, making it possible to observe the covariation relationships between them. Others occur at an attentional distance, obscured by space or other variables that make it difficult to perceive covariation. The term action at an attentional distance (A@AD) underscores this distinction (Grotzer & Solis, 2015). Previous research demonstrated that elementary students experience difficulties in interpreting A@AD but can reason about it through mediating mechanisms. The present study extended this work to characterize kindergarteners’ reasoning about A@AD within familiar and unfamiliar contexts. Twenty-five kindergarteners participated in two interview sessions where they were presented with hypothetical scenarios and asked to reason about the possibility of A@AD. Results revealed that in certain cases young students accepted and described A@AD, and this was informed by their familiarity with the context, availability of possible explanatory mechanisms, access to covariation information, and attention to their own interventions.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".