Diversity in Canadian election-related Twitter discourses: Influential voices and the media logic of #elxn42 and #cdnpoli hashtags
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it