Impacts of Digital Media Literacy Skills on the Accuracy of Truth Discernment
Bibliographic record
Abstract
This paper is a segment of a larger dissertation exploring the impact of digital media literacy (DML) skills on the accuracy of truth discernment. The purpose of this paper is to offer broader access to the findings and contribute to the discussions of disinformation, focusing on the significance of the accuracy of truth discernment in politics and law. As earlier studies have examined, the influx of disinformation in the digital age was a pressing global security threat, spreading rapidly through social media platforms. Disinformation, consisting of the deliberate spread of falsehoods, causing chaos and confusion eroded trust in media and government, driving citizens to believe falsehoods to be true, particularly in the absence of DML to discern the reliability of information. This study supports earlier research, revealing that simplifying access to credible information empowers individuals to retrieve trustworthy sources. The qualitative content analysis conducted in this study shows that DML skills shape truth-seeking behaviors, finding high correlations between DML skills and informed political participation. The findings of this research delineate the theoretical mechanisms of how DML skills empower individuals to engage in civil society by synthesizing themes described by scholars within the top 100 cited sample studies selected. Future researchers can assess the theoretical mechanisms outlined in this study to determine their effectiveness by implementing training programs to develop foundations for informed decision-making, political participation, and responsible sharing behavior.
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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 | 0.009 | 0.167 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".