Identifying CRAAP on the Internet: A Source Evaluation Intervention
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.048 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.009 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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 it