Bibliographic record
Abstract
Few books in Canada empirically and systematically examine the role journalists play in the news-making process. While there are several books that look at how journalists do their jobs, and others that examine the political process, none - until now - have analyzed the opinions of journalists and how the news is reported. Focusing primarily on the political orientation of journalists, Miljan and Cooper investigate the link between what journalists believe about politics and how they report political issues. Using data gathered from interviews with over 800 Canadians and some 270 journalists, the authors compare how the attitudes of journalists differ from those of the general population, and how the journalists’ opinions influence the daily news. By examining the way they respond to questions on the economy, social issues, and national unity, and comparing these responses with how the stories were reported in Canadian news outlets, the book arrives at the controversial conclusion that journalists, moreso than the owners of the media, are the architects of the news, engineering not only its drama, but also its ideological thrust. A must-read for anyone interested in politics and the media, this book should be read by journalists, politicians, academics, and all Canadians who are concerned about the hidden agendas of journalists.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.012 | 0.010 |
| Scholarly communication | 0.015 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.064 | 0.010 |
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".