Distant lands make for distant possibilities: Children view improbable events as more possible in far-away locations.
Bibliographic record
Abstract
= 300) might have more success in recognizing that these events are possible if they considered whether the events could happen in a distant country. Children heard about improbable and impossible events (Experiments 1A, 1B, and 2) and about ordinary events (Experiment 2) and either judged whether the events could happen in a distant country or locally (Experiments 1A and 2) or with their location unspecified (Experiment 1B). Children were more likely to judge that extraordinary events could happen in a distant country than when the same events were described locally or with location unspecified; also, older children were more likely to deny these events could happen when they were local compared with when their location was unspecified. We also found some evidence that manipulating distance affects judgments more strongly for improbable events than for impossible one. Together, the findings show that children's assessments of whether hypothetical events are possible are affected by the geographic context of the events. The findings are consistent with accounts holding that children normally assess whether hypothetical events are possible by drawing on their knowledge of the ordinary world but further suggest that children modify this approach when considering events in distant lands. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 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".