How Canadians Communicate IV: Media and Politics
Bibliographic record
Abstract
Substantial changes have occurred in the nature of political discourse over the past thirty years. Once, traditional media dominated the political landscape, but in recent years Facebook, Twitter, blogs and Blackberrys have emerged as important tools and platforms for political campaigns. While the Canadian party system has proved surprisingly resilient, the rhythms of political life are now very different. A never-ending 24-hour news cycle has resulted in a never-ending political campaign. The implications of this new political style and its impact on political discourse are issues vigorously debated in this new volume of How Canadians Communicate, as is the question on every politician?s mind: How can we draw a generation of digital natives into the current political dialogue? With contributions from such diverse figures as Elly Alboim, Richard Davis, Tom Flanagan, David Marshall, and Roger Epp, How Canadians Communicate IV is the most comprehensive review of political communication in Canada in over three decades ? one that poses questions fundamental to the quality of public life.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.012 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".