Tweets and Truth: Journalism as a Discipline of Collaborative Verification
Bibliographic record
Abstract
This paper examines how social media is influencing the core journalistic value of verification. Through the discipline of verification, the journalist establishes jurisdiction over the ability to objectively parse reality to claim a special kind of authority and status. Social media questions the individualistic, top-down ideology of traditional journalism. The paper considers journalism practices as a set of literacies, drawing on the theoretical framework of new literacies to examine the shift from a focus on individual intelligence, where expertise and authority are located in individuals and institutions, to a focus on collective intelligence, where expertise and authority are distributed and networked. It explores how news organizations are negotiating the tensions inherent in a transition to a digital, networked media environment, considering how journalism is evolving into a tentative and iterative process, where contested accounts are examined and evaluated in public in real-time.
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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 | 0.025 | 0.137 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.008 | 0.030 |
| Scholarly communication | 0.028 | 0.027 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".