Adolescents’ credibility justifications when evaluating online texts
Bibliographic record
Abstract
Research has shown that students differ in their abilities to evaluate the credibility of online texts, and, in general, many perform poorly on online evaluation tasks. This study extended current knowledge by examining students' abilities to justify the credibility of online texts from different perspectives, thus providing a more nuanced understanding of students' credibility evaluation ability. We examined how upper secondary school students (N = 73; aged 16 to 17) evaluated author expertise, author intention, the publication venue, and the quality of evidence when reading four texts about the effects of sugar consumption in a web-based environment. Additionally, we examined how students' prior topic knowledge, Internet-specific justification beliefs, and time on task were associated with their credibility justifications. Students evaluated author expertise, author intention, the venue, and the quality of evidence for each text on a six-point scale and provided written justifications for their evaluations. While students' credibility evaluations were quite accurate, their credibility justifications lacked sophistication. Inter-individual differences were considerable, however. Regression analysis revealed that time on task was a statistically significant unique predictor of students' credibility justifications. Instructional implications are discussed.
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.
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 | 0.078 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".