Bibliographic record
Abstract
Should we take tweets from politicians seriously? This paper argues that tweets sent out from the accounts of the top political actors are important because they are framed within a worldview that looks to support or challenge the legitimacy of an institutional order. As Twitter provides a direct connection between the speaker and mass audiences, it offers political leaders a platform to articulate a worldview, justify democratic or undemocratic strategies for competition, and mobilize support across frontiers to influence the perception of power structures. The relationship between discourse and institutional legitimacy is especially important in systems like Venezuela’s where authoritarian and democratic practices coexist, meaning that the legitimacy of institutions largely depends on the agency of key actors in influencing the perception of what is considered to be democratic. Therefore, this study carries out a content analysis of the tweets of the opposition and incumbent Venezuelan leaders. The results show that the incumbent’s discourse was predominantly framed within a populist worldview, which perceives politics as a zero-sum struggle between the people and a conspiring global elite, such that the incumbent’s infringements on democratic procedures were justified as an effort for emancipation from global oppressors. The opposition articulated a pluralist discourse that defended electoral competition, understood as the way to resolve the various interests and goals of a heterogeneous society, and therefore resorted to democratic strategies to challenge the incumbent’s power. Given the unprecedented reach of social media, this study highlights the extent to which Twitter contributes to materialize an interpretation of power structures, and how political elites use it to influence the legitimacy of an institutional order.
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.014 | 0.018 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 0.003 |
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".