The Threat of Misinformation on Journalism’s Epistemology: Exploring the Gap between Journalist’s and Audience’s Expectations when Facing Fake Content
Bibliographic record
Abstract
This study analyzes the discourse of reporters, editors and audiences in focus groups and in-depth interviews, examining the expectations on journalists when facing misinformation. While both groups agree that journalistic information is critical, how this expectation is met varies. On the one hand, the audience’s way of knowing involves diverse assessments regarding valuable information; also, they are dubious about journalists’ intentions. On the other hand, journalists exhibit a limited understanding of the audience’s informational needs and encounter practical challenges in rigorously fact-checking, affecting their authority in knowledge generation. The study proposes a discussion on acknowledging their complex epistemologies to benefit mutual understanding. Doing this can establish structural support for journalistic information, contributing to trust in journalism when challenged by sources spreading misinformation.
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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 | 0.051 | 0.154 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.008 | 0.018 |
| Scholarly communication | 0.016 | 0.015 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".