Mitigating Distrust in Digital News Media: A Qualitative Research-Creation Study of a Proposed Fact-Checking Decentralized Application
Bibliographic record
Abstract
As mis- and disinformation is created and shared with greater ease and speed, trust in online news media is declining. While the “infodemic” resulting from the COVID-19 pandemic is the most obvious recent example of this (Garneau & Zossou, 2021), the spread of mis- and disinformation is woven into conflicts around the globe, from the Russian invasion of Ukraine (Price, 2022; O’Neill, 2022), to divisive domestic tensions, like climate denial (Meyer, 2021). Reuters puts Canadians’ trust in “news overall” at 42 per cent (Newman et al., 2022, p. 119). While other studies provide a more optimistic overview (Bricker, 2021; Edelman Canada 2021), they still show that trust is an issue of concern. This paper will explain how a fact-checking decentralized application (dApp) could provide a democratic process for recording and verifying claim reviews, and through crowdsourcing verification and fostering transparency and digital media literacy, potentially help bolster public trust in news media. This paper is one component of a Research-Creation Thesis Project, which also consists of: (a) a website, checkmarker.org, that explores key problems negatively impacting trust and how a fact-checking dApp might work; (b) interviews with professionals with expertise relevant to trust in news media and/or blockchain; and (c) a short survey to gather preliminary feedback about the concept and the topic of trust in journalism. In the future, this project could lead to the development of a prototype of a fact-checking dApp, as well as be used to make a case for or against the use of blockchain in journalism.
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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 | 0.038 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.013 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".