A Hybrid Analysis of the State of Automated Journalism in Canada:Current Impact and Future Implications for Journalists and Newsrooms
Bibliographic record
Abstract
In recent years, certain media organizations around the world have begun adopting automated journalism to enhance their newsrooms’ productivity and reporting capacity. These tools, which can be fully- or semi-autonomous expert systems, are being implemented for a multitude of reasons, such as to ease journalists’ workflow, to expand news coverage, to uncover complex investigations and to cut costs. While some have argued automation can help increase newsroom efficiency and output, questions regarding authorship and transparency, and the implications for journalists, have come into question. Despite automated journalism’s growing presence, little is known about its integration in and influence on the Canadian news media landscape. This thesis reports on the results of qualitative interviews with nine journalists and news media professionals and aims to examine how automated journalism techniques and tools are being integrated, and the effects they have on journalists, their practices, methods, and what is being demanded of them. The purpose of this thesis is to fill a gap in the literature and in our knowledge about automated journalism’s role in Canada. Its findings conclude that, although Canada is behind in the adoption of automated journalism, there is an overwhelming consensus that the ethical guidelines of the Canadian news media industry need to be revised in order to better frame the use of automated technologies.
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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 | 0.018 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.030 |
| Science and technology studies | 0.017 | 0.009 |
| Scholarly communication | 0.017 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".