En-garde: Source Evaluations in the Digital Age
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
Students have difficulty assessing the quality of information. They often rely on content-focused criteria to make reliability assessments and, as a result, may accept inaccurate information. Despite the impact of poor source evaluation skills, educational researchers have not widely examined source evaluation behaviours in authentic environments or tasks. Students’ epistemic cognition, or their thinking about the epistemic properties of specific knowledge claims and sources, is one promising avenue to better understand their source evaluation behaviours. Two studies were conducted to explore students’ epistemic thinking. In Study 1, college students (n = 12) reported their reliability criteria in focus group interviews. Four of these participants (n = 4) also examined the reliability of an online news article. Grounded theory was used to infer students’ epistemic ideals and reliable epistemic processes. In Study 2, students (n = 43) rank-ordered two news articles and justified how they assigned each article’s rank in a written response. Most students were able to accurately rank-order the articles using relevant epistemic processes. Cluster analysis was used to characterize the evaluation criteria used. Surprisingly, more participants who justified their decisions using relevance criteria accurately rank-ordered the articles. The role of direct and indirect indicators of reliability are discussed through the lens of the Apt-AIR framework of epistemic thinking.
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 | 0.010 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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