Bibliographic record
Abstract
Media relations is one of the mainstays of the communications business.With the proliferation of news media in the past two decades (all-news television channels, all-news radio stations, a second national newspaper, digital sites like The Tyee, Buzzfeed Canada), more opportunities exist than ever before for being "in the news" and using news media as a means to communicate your issues to the public at large or to very targeted groups.In the past, most communicators who specialized in media relations came from a media background: former reporters who by experience and instinct knew how the media worked, what they needed and what they responded to.As with journalism itself, however, more communicators are coming to the profession through the academic route, taking formal degree and certificate programs offered by universities and colleges.In Canada, about one hundred such programs exist, often as a part of the same faculty, such as the School of Journalism and Communication at Carleton University in Ottawa.What students of communications lack is a standard textbook on media relations that meets academic standards, is research-based and provides both a practical and a philosophical guide to dealing with media and reporters.Trade books on media relations have been written, but they are of varying quality or focus on specific aspects of media relations (e.g., for business, for advocacy groups, or on how to write a news release).1 The experience practitioners bring to the field is valuable, and introduction
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.537 | 0.389 |
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".