Young children use supply and demand to infer desirability.
Bibliographic record
Abstract
In 4 experiments, we show that young children (total N = 290) use information about supply and demand to infer the desirability of resources. In each experiment, children saw scenarios about sandwiches from different shops, which varied in supply (number of sandwiches produced for the day) and demand (number of customers attracted). In Experiments 1 and 2, 5- to 6-year-olds gave higher desirability ratings for sandwiches from shops with greater than lesser demand when supply was held constant. In Experiment 3, 5- to 7-year-olds gave higher desirability ratings for sandwiches from shops with less than more supply when demand was held constant. Finally, in Experiment 4, 5- to 6-year-olds were more likely to judge that sandwiches came from a good shop (rather than from a bad one) when demand exceeded supply than when supply exceeded demand. Together, the findings reveal a way that children can infer how desirable resources are, without needing to incur the costs that would normally be required to obtain and sample the resources themselves. (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 | 0.003 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".