Children’s ability to edit their memories when learning about the environment from credible and noncredible websites
Bibliographic record
Abstract
One of the many sources of information easily available to children is the internet and the millions of websites providing accurate, and sometimes inaccurate, information. In the current investigation, we examined children's ability to use credibility information about websites when learning about environmental sustainability. In two studies, children studied two different websites and were tested on what they had learned a week later using a multiple-choice test containing both website items and new distracters. Children were given either no information about the websites or were told that one of the websites (the noncredible website) contained errors and they should not use any information from that website to answer the test. In both studies, children aged 7- to 9-years reported information from the noncredible website even when instructed not to, whereas the 10- to 12-year-olds used the credibility warning to 'edit out' information that they had learned from the noncredible website. In Study 2, there was an indication that the older children spontaneously assessed the credibility of the website if credibility markers were made explicit. A plausible explanation is that, although children remembered information from the websites, they needed explicit instruction to bind the website content with the relevant source (the individual websites). The results have implications for children's learning in an open-access, digital age where information comes from many sources, credible and noncredible. Education in credibility evaluation may enable children to be critical consumers of information thereby resisting misinformation provided through public sources.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 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".