Bibliographic record
Abstract
Confronted daily with decisions on how to present their stories, what to write and what not to write, journalists and the media are frequently accused of sensationalizing, of choosing to report the bad news, and of misquoting those they interview. In this substantially updated edition of Morals and the Media, Nick Russell addresses many of the concerns the public has about the media as he examines why the media behave the way they do. He also discusses how values have been developed and applied and suggests value systems that can be used to judge special situations. This revised edition covers the many changes in the Canadian media in the last decade, including further concentration of media ownership, media convergence, online journalism, the rise of the web log, and the tightening economic pressures on the industry as a whole. While much of the debate in this field has focused on conditions in the United States, Russell points out that the ethical issues that arise in Canada are often substantially different from those in the US. He has also added new “Tough Calls” at the end of each chapter, inviting readers to test their own ethics in scenarios drawn from real news stories. Morals and the Media will be essential reading in journalism courses as well as an important resource for journalists. It will also be of interest to the consumers of journalism – the readers, listeners, and watchers – who wonder why the media do what they do.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.028 | 0.013 |
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".