PANDEMIC POLITICS: THE 2021 AND 2022 GERMAN AND AUSTRALIAN FEDERAL ELECTION CAMPAIGNS ON SOCIAL MEDIA
Bibliographic record
Abstract
One of the effects of the continuing COVID-19 pandemic has been to further accelerate the incorporation of social media activities into political and electoral campaigning. Especially as a result of lockdowns and other restrictions to offline public life, overall social media use has increased in many countries; health concerns have severely curtailed conventional in-person political campaigning activities, from doorknocking to mass rallies (even if some candidates are openly flouting health measures in order to appeal to fringe, COVID-denialist voters); and concerns about the safety of in-person voting processes have also led to a growth in postal voting well ahead of election day, potentially increasing the importance of political messaging early on in election campaigns. In addition, of course, the pandemic itself, and the health, economic, and social measures taken by different governments to address and manage its implications, have also become a dominant theme in most political contests. Political parties around the world have scrambled to keep up with and engage with these changing circumstances, voter behaviours, and political debates, and it is therefore time to re-examine the current state of affairs. This panel does so by focussing on social media campaigning in two of the most recent major national elections: the German federal election campaign in August and September 2021, and the Australian federal election campaign in March to May 2022. The four papers included in this panel examine political campaigning, public engagement, and journalistic coverage on Facebook and Twitter, as well as political advertising practices on Facebook, and in combination offer a very timely new perspective on electioneering in the final stages of a multi-year global pandemic.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".