Bibliographic record
Abstract
The idea for this book came to me several years ago, when I was preparing to teach a class in media relations at the University of Alberta's Faculty of Extension.I received the standard form for instructors that asked, among other things, if I had a textbook I required students to use for the course.As always, I ticked the box that said "No," then wondered why such a book wasn't available.A number of trade books on media relations have been published, but they tend to be overly specialized (e.g., media relations for small business, how to write a news release), too theoretical (media as part of the militaristic machine) or simply too personal (recitations of old media war stories, long on personal experience but short on the considerable research that has been done on news media in North America and the world).In looking at the stack of photocopies I had prepared for the university to hand out to the students, and the amount of research I had collected over the years, it dawned on me that there was a need for a basic text on media relations.Such a book would provide an introduction to media relations, grounded in both experience and research, and would reflect current media and communications practices.While primarily intended for students in university and college communications programs, it would also be of benefit to practitioners and aspiring practitioners of media relations in the private, public and voluntary sectors.It would also
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.505 | 0.344 |
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".