Social media and democracy: How the Facebook usage patterns of Toronto city councilors influence political engagement
Bibliographic record
Abstract
In the early days of the Internet, many political communication theorists held the utopian belief that political actors would use online tools to communicate directly with members of the public, and thereby bolster political engagement and enrich democracy. Unfortunately, studies over the past two decades found that political websites were not usually used to interact directly with the public, but instead were used to simply disseminate information in a one-way information-sharing model. However, the emergence of social media sites presents political actors with the opportunity to interact with the public far more easily than websites had previously allowed. Given the widespread adoption and high usage rates of social media sites, these online resources could potentially open up a space for public discussion about politics and allow political actors to interact directly with members of the public. Literature indicates that this type of shared space is conducive to the kind of civic mindset that leads to higher rates of political engagement. Research on political uses of social media tends to focus on the use of social media engagement. Research on political uses of social media tends to focus on the use of social media within elections, such as the 2008 U.S presidential election, and on the use of social media by national governments. I have chosen instead to examine how a group of municipal councilors in Toronto, Ontario uses social media. These politicians have the greatest need to interact directly with individuals throughout their term of service because municipal councilors are expected to know the members of their ward far more intimately than federal, or even provincial, politicians. My study focuses on the use of Facebook because literature indicates that it is the most political social media platform and that it presents politicians with the greatest opportunity to foster political engagement online. Through analysis of the Facebook pages of Toronto city councilors this study examines the degree to which councilors use Facebook to engage their followers, whether certain citizens are consistently engaged in ongoing political discussions, and whether small communities of politically engaged citizens develop around the Facebook profiles of councilors.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".