Identifying CRAAP on the Internet: A Source Evaluation Intervention
Bibliographic record
Abstract
Individuals of all ages struggle to determine the reliability of information on the internet. To address this common issue, many educational institutions have endorsed the CRAAP test (i.e., currency, relevance, authority, accuracy, and purpose) as an effective approach to support identification of unreliable information. The present study extended the CRAAP test by incorporating a modeling component on how to evaluate and integrate multiple sources of varying quality on the internet and evaluated the efficacy of this source evaluation training intervention. Eighty-two participants across Canada were recruited to evaluate six authentic webpages and then construct an argument on a specific topic. Half the sample received training to examine the currency, relevance, authority, accuracy, and purpose (i.e., CRAAP) of the webpages before completing the online activity. Results revealed that the intervention group provided more accurate rank-ordering of the webpages, but no differences were found between groups on source integration via an argumentative essay. These findings suggest that the CRAAP test is effective in improving individuals’ evaluations of online sources but is not effective in promoting better quality source integration.
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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 | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".