Diversity in Canadian election-related Twitter discourses: Influential voices and the media logic of #elxn42 and #cdnpoli hashtags
Bibliographic record
Abstract
Using qualitative and quantitative content analysis of Twitter, this study examined 5,209 tweets with popular hashtags #elxn42 and #cdnpoli to determine what was discussed on the social media platform one week preceding the 2015 Canadian federal election. Searching for diversity-related issues, researchers asked whether diverse groups were represented among the most influential accounts. It also identified the most common topics shared, and whether the shared content represented democratic discussion. Finally, the study looked at how much election-relatedsharing among influencers conformed to a media logic or social media logic framework. Researchers found that Twitter use during the election campaign did not provide a level playing field for political discussion. Instead, data suggested individual celebrity users were more likely to be amplified than others. Despite this, however, it appears that issues that were relevant to diverse groups made it into the Twitter conversation, making up a meaningful portion of tweets related to the election. These findings suggest that if diverse voices were not retweeted, at least issues were still being discussed, and thus contradict the popular idea of online echo chambers on Twitter.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.035 | 0.009 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".