Speaking Truth to Power: Twitter Reactions to the Panama Papers
Bibliographic record
Abstract
The current study examines the micro-linguistic details of Twitter responses to the whistleblower-initiated publication of the Panama Papers. The leaked documents contained the micro-details of tax avoidance, tax evasion, and wealth accumulation schemes used by business elites, politicians, and government bureaucrats. The public release of the documents on April 4, 2016 resulted in a groundswell of Twitter and other social media activity throughout the world, including 161,036 Spanish-language tweets in the subsequent 5-month period. The findings illustrate that the responses were polyvocal, consisting a collection of overlapping speech genres with varied thematic topics and linguistic styles, as well as differing degrees of calls for action and varying amounts of illocutionary force. The analysis also illustrates that, while the illocutionary force of tweets is somewhat associated with the adoption of a prosaic and vernacular ethical stance as well as with demands for action, these types of voicing behaviors were not present in the majority of the tweets. These results suggest that, while social media platforms are a popular site for collective forms of voicing activities, it is less certain that these collective stakeholder voices necessarily result in forceful accountability demands that spill out of the communication medium and thus serve as an impulse for positive social change.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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; 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".