Bibliographic record
Abstract
On May 2, 2011, Canadians voted in what the news media dubbed “Canada's First Social Media Election.” This allowed Canadians to join their neighbours to the south who, arguably, had gone through one national social media election during the 2008 bid for the presidency. Through a theoretical discussion of what constitutes sociality and networked sociality, and a critical examination of social media as a campaign tool, this chapter asks “What makes a campaign social?” It also asks if the term “social media campaign” adequately describes current campaign practices? In exploring these questions, the chapter draws on the 2011 federal election in Canada and the 2008 American election. Ultimately, the chapter argues we have limited evidence that social media has led to increased sociality when it comes to electoral politics. This calls the appropriateness of the term “social media campaign” into question. Such lack of evidence stems from the dynamism of networked sociality, which renders it difficult to understand, and methodological difficulties when it comes to capturing what it means to be “social.”
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".