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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".