“There’s a Rule Book in my Head”: Journalism Ethics Meet A.I. in the Newsroom
Bibliographic record
Abstract
The burgeoning use of artificial intelligence (A.I.) to create journalistic products is challenging the ethical standards in Canadian newsrooms and calling into question the efficacy of existing norms and practice worldwide. Ethical literacy related to the use of A.I. remains low in the industry at large, and with no standardized ethical practice, there is little understanding of how journalistic doxa might need to expand to keep up with technology. Ensuring ethical practice is becoming more critical in a polarized political climate where mis- and disinformation abound, audiences demand transparency, and the very boundaries and definitions of journalism are contested by both journalists and their audiences. Utilizing field theory, through interviews with journalists and analysis of published codes of ethics and existing literature, this article examines how Canadian newsrooms are using A.I., and whether ethical frameworks are adequately evolving alongside technology.
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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 | 0.010 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.029 | 0.037 |
| Scholarly communication | 0.021 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".