Bibliographic record
Abstract
TikTok has revolutionized the ideation and positioning strategies of the contemporary political class. The desire for visibility and recognition among politicians has sparked a keen interest in utilizing this social network to promote a series of discourses, messages, and actions aimed at offering a new way of doing politics through diverse microformats tailored for different audiences. In this context, the present article seeks to identify the social representations constructed by the candidates for the Presidency of the Republic (2024-2029) and the Legislative Assembly (2024-2027) on TikTok in the lead-up to the 2024 elections in El Salvador. Based on a mixed-method approach, which relies on the triangulation of bibliographic sources, as well as the collection, analysis, and visualization of data, this research highlights how digital and traditional arenas merge entertainment and politics, significantly impacting voter engagement and transforming electoral communication into a more emotional and experiential phenomenon, contrasting with the objectivity and rationality traditionally expected when making an informed voting decision. EMH ABIERTA_II_N17-2023-30-67.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.016 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.613 | 0.500 |
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".