CyberCampaigns and Canadian Politics: Still Waiting?
Bibliographic record
Abstract
The early election call in the fall of 2000 provided the perfect opportunity to study the impact the Internet has had on election campaigning in Canada. With the explosion of use the Net has seen since the 1997 general election, Canadian federal parties stood at the threshold of a new age in election campaigning. Pundits such as Rheingold (1993) have argued that the Internet will provide citizens with a way to bypass traditional media and gain unmediated access to each parties political message as well as providing a forum for citizens to engage the parties, and each other in deliberative debate. Through a longitudinal analysis of party web pages and telephone interviews with party staffers, we analyze the role the Internet played in the election campaigns of Canada's federal parties. Our findings indicate that the parties are still focusing on providing online features that talk at the voter instead of engaging them in any type of meaningful discourse. Most of these sites were exceptionally similar in their structure and in the type of content they provided. Generally, these sites served as digital archives for campaign material created with other media in mind and despite the multimedia capabilities of the Internet, these sites tended to be overwhelmingly text oriented. In line with Stromer-Galley's (2000) discussion of why candidates in the U.S. avoid online interaction, we also argue that little incentive exists to motivate parties to engage in any meaningful interaction with voters online.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.013 |
| Science and technology studies | 0.028 | 0.012 |
| Scholarly communication | 0.014 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.023 | 0.001 |
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".