Bibliographic record
Abstract
The follow-up to 2003's How Canadians Communicate, this second volume embarks upon a new examination of Canada's current media health and turns its attention to the impact of globalization on Canadian communication, culture, and identity. How Canadians Communicate, Vol. 2: Media, Globalization and Identity, includes contributions from experts from a wide range of specialties in the areas of communication and technology. Some, as the editors point out, are optimistic about the future of Canadian media, while others are pessimistic. All, however, recognize the profound impact of rapidly changing technologies and the new globalized world on Canadian culture. The contributors highlight the new tools such as blogs, Blackberries, and peer-to-peer networks that are continuously changing how Canadians communicate. And, they explore the various ways in which Canada is adapting to the new climate of globalization, suggesting new and innovative paths to further define and strengthen our uniquely Canadian cultural identity. With Contributions By: Maria Bakardjieva Bart Beaty Helen Clarke Christopher Dornan Kenneth J. Goldstein Sheryl N. Hamilton Michael Keren Stephen Kline Graham Longford David Mitchell Frits Pannekoek Marc Raboy Richard Schultz Will Straw Rebecca Sullivan Richard Sutherland David Taras Andrew Waller
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.014 | 0.010 |
| Scholarly communication | 0.014 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.029 | 0.005 |
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".