Examining the structure of credibility evaluation when sixth graders read online texts
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
Abstract Background Previous research indicates that students lack sufficient online credibility evaluation skills. However, the results are fragmented and difficult to compare as they are based on different types of measures and indicators. Consequently, there is no clear understanding of the structure of credibility evaluation. Objectives The present study sought to establish the structure of credibility evaluation of online texts among 265 sixth graders. Methods Students' credibility evaluation skills were measured with a task in which they read four online texts, two more credible (a popular science text and a newspaper article) and two less credible (a layperson's blog text and a commercial text). Students read one text at a time and evaluated the author's expertise, the author's benevolence and the quality of the evidence before ranking the texts according to credibility. Four competing measurement models of students' credibility evaluations were assessed. Results The model termed the Genre‐based Confirming‐Questioning Model reflected the structure of credibility evaluation best. The results suggest that credibility evaluation reflects the source texts and requires two latent skills: confirming the more credible texts and questioning the less credible texts. These latent skills of credibility evaluation were positively associated with students' abilities to rank the texts according to credibility. Implications The study revealed that the structure of credibility evaluation might be more complex than previously conceptualized. Consequently, students would benefit from activities that ask them to carefully analyse different credibility aspects of more and less credible texts, as well as the connections between these aspects.
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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 | 0.002 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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