Don?t Trust #CDNMedia: Twitter Posts From Eight Canadian Communities During #elxn42
Bibliographic record
Abstract
Originally designed for networking and to deliver mostly inconsequential information, social media is becoming more prevalent in political landscapes [81], while traditional local news environments are diminishing. Part of a global trend, local news outlets in Canada are closing faster than new ones spring up to replace them [51]. This trend is concerning in light of a report by the Knight Commission [46], which described the availability of local information as something which is "as vital to the healthy functioning of communities as clean air, safe streets, good schools, and public health" [46]. When the news being shared is political in nature, one can argue that it is uniquely vital to society, as political news helps citizens make informed decisions, particularly during election time [46]. As traditional media outlets close, and particularly in light of recent Facebook algorithm changes, many people turn to alternative sources of news, like Twitter to find out about current and politically relevant information in their communities [36], [58]. This trend presents us with questions: Does Twitter currently function as an alternative political news source for communities outside major media centers, particularly when traditional news outlets are being closed? And if not, how is it currently functioning with respect to election news?
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.046 | 0.011 |
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".