Fundamentals of Public Relations and Marketing Communications in Canada
Bibliographic record
Abstract
Experts in public relations, marketing, and communications have created the most comprehensive textbook specifically for Canadian students and instructors. Logically organized to lead students from principles to their application—and generously supplemented with examples and case studies—the book features chapters on theory, history, law, ethics, research methods, planning, writing, marketing, advertising, media, and government relations, as well as digital, internal, and crisis communications. Chapters open with learning objectives and conclude with lists of key terms, review and discussion questions, activities, and recommended resources. Fundamentals of Public Relations and Marketing Communications in Canada will be essential in post-secondary classes and will serve as a valuable reference for established professionals and international communicators working in Canada. Foreword by Mike Coates. Contributors: Colin Babiuk, Sandra L. Braun, Wendy Campbell, John E.C. Cooper, Marsha D’Angelo, Ange Frymire Fleming, Mark Hunter LaVigne, Danielle Lemon, Allison G. MacKenzie, Sheridan McVean, Charles Pitts, David Scholz, Jeff Scott, Charmane Sing, Amy Thurlow, Carolyne Van Der Meer, Ashleigh VanHouten, Cynthia Wrate, and Anthony R. Yue. Sponsor: Hill + Knowlton Strategies
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.051 | 0.016 |
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".