Tones from a Narrowing Race: Polling and Online Political Communication during the 2014 Scottish Referendum Campaign
Bibliographic record
Abstract
The use of negative political communication is a predominant characteristic of modern politics. However, literature doesn't provide an answer to the following question: what explains fluctuations in the use of negative messages within political organisations during a given political campaign? The present paper examines this question in the context of the 2014 Scottish independence referendum. Data consists of all tweets distributed by the official Twitter account of both campaign organisations ( @YesScotland and @UK_Together ) between June 16, 2014 and September 17, 2014. Results are obtained by a non-parametric local regression and by time-series regression analyses. Our model demonstrates that having an advance in the polls had a statistically significant influence on the tweet sentiment of at least one organisation during the referendum campaign: Better Together's messages were more negative when it was ahead in the polls. Meanwhile, Yes Scotland's messages were more negative after each of the leaders' debates.
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How this classification was reachedexpand
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".