Is No Election News Good News? The 2015 Canadian Election and Locally Relevant News on Twitter
Bibliographic record
Abstract
This study uses the 2015 Canadian Federal election as a case study to examine whether Twitter is used to spread locally relevant political news in Canadian communities outside major urban centres in the month leading up to an election. We examined eight communities across Canada, each with differing levels of traditional local media access (television, radio, and print). We wanted to discover, particularly in communities underserved by traditional local media, whether Twitter would help to fill an information gap during election time by helping to spread locally relevant political information. Preliminary analysis has revealed that most information shared on Twitter accounts in our eight communities was national rather than local in scope. Influencers, as identified by the number of @-mentions, tended to be national, rather than local, and general activity on Twitter did not reflect overall population of an area, or any specific locally important issue. Thus we conclude that despite its potential, Twitter is currently not a useful counterbalance for a declining local traditional news environment in smaller communities across Canada.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".