The dynamics of negativity in media outlets during the Greek sovereign bond crisis
Bibliographic record
Abstract
It is well known that the media display an asymmetric reaction to real-world events, which results in the prioritisation of negative coverage. However, there is still much to discover regarding the qualitative distinctions between different media types and the dynamics of negativity. This article investigates how different media outlets framed Greece in evaluative terms during and after the Sovereign Bond Crisis relying on 12,376 articles published in the British press between 2009 and 2018. The study confirms earlier findings that the ‘negativity bias’ differs across media types in terms of the level of negative tone. In addition, the study’s significant contribution is to highlight the persistence of negativity. Fractional integration time series econometrics is employed to assess the extent to which tonality persists over time. As theorised, all the time series of tonality exhibit long-term memory. Moreover, some evidence is found of differentials in negativity persistence across media outlet types.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".