Fake news and fact-checking: Combating misinformation and disinformation in Canadian newsrooms and journalism schools
Bibliographic record
Abstract
This exploratory study investigates how the global COVID-19 pandemic spotlighted fact-checking to combat misinformation and disinformation in Canadian journalism. Specifically, this work investigates how Canadian journalists and journalism educators may be approaching fact-checking (both ante hoc, or editorial, and post hoc) to respond to more forms of misinformation and disinformation. Through expert in-depth interviews (n = 14) with Canadian journalism educators, reporters, and newsroom leaders, this analysis sketches an initial understanding of the place of fact-checking in Canadian journalism practice and pedagogy. This initial study offers five tentative findings from our expert interviews: (1) while the COVID-19 pandemic highlighted the need for more fact-checking, Canadian journalists and journalism educators believe the worldwide health crisis was not the sole trigger for an increased focus on fact-checking in Canadian journalism and journalism education; (2) over the last decade, Canadian journalism schools may have increased their focus on fact-checking and verification teaching; (3) while Canadian newsroom leaders want their journalists to have solid fact-checking and verification skills to combat concerns about information integrity, they are concerned about the skills new graduates bring to the job; (4) Canadian journalists and journalism educators believe ante hoc or editorial and post hoc fact-checking should play a more significant role in Canadian journalism; and (5) while there is concern about the efficacy of post hoc fact-checking (whether it corrects misconceptions), Canadian journalists and journalism educators appear committed to the practice because of normative and democratic ideals surrounding truth and information integrity. Moreover, this exploratory inquiry highlights the essential democratic work of Canadian journalism to combat misinformation and disinformation.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.010 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".