Unpacking credibility evaluation on digital media: a case for interpretive qualitative approaches
Bibliographic record
Abstract
Abstract We argue for more serious consideration of interpretive qualitative approaches in research on information credibility evaluation in digitally mediated contexts. Through reviewing existing literature on credibility and drawing on our own experiences of conducting research projects on credibility evaluation in diverse cultural contexts, we contend that interpretive qualitative approaches help researchers develop a much-needed communicative and relationally and culturally situated understanding of credibility, complicating dominant quantitative and psychologically-oriented accounts. We detail how these approaches add important nuance to how credibility is conceptualized and operationalized and reveal the complexity of credibility evaluation as a social process. We also outline how they aid researchers studying misinformation engagement, especially in popular bounded social media places like private groups and chats. The approach we develop here provides new insights that can inform ongoing global efforts by researchers, policy makers, and citizens to more fully understand the complexity of information verification online.
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How this classification was reachedexpand
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 | 0.007 | 0.039 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".