Bibliographic record
Abstract
As political engagement declines in Western democracies, the Internet has been held up as a promising site for citizen participation and engagement. This optimism has been fuelled by recent political events that seem to confirm the Internet's democratic potential. Barack Obama channelled the Internet's power for fundraising and voter mobilization in the 2009 U.S. election. Likewise, Iranian voters successfully used social media such as Twitter to organize protests of the country's 2009 presidential election. This paper presents a first look at how Canadian political parties are using and responding to online communication tools during elections campaigns. Specifically it examines the role of online communications tools in building and developing a campaign platform. Moreover, it discusses whether these activities represent a shift towards a strengthened democracy or are simply reflective of current political culture. The findings are based on data gathered through semi-structured interviews with political strategists involved in the 2008-09 federal, British Columbia provincial and Vancouver municipal elections. This study found that online communication during election campaigns has little influence on the shape of the policy platform. However, political parties have been quick to adopt new online communications platforms allowing them to market their candidates and policies. Moreover, the Internet has shaped traditional campaign functions allowing parties to recruit funds, voter information and volunteers online. Rather than fundamentally shifting the character of democracy in Canada, the current use of online communication tools seems to be defined by the existing political culture.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.014 | 0.002 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.000 |
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".