El meme como herramienta para informar desde los medios en línea de ecuador: Caso el comercio y la hora Tungurahua
Bibliographic record
Abstract
ABSTRACT The present study analyzes the characteristics of memes published by El Comercio and La Hora Tungurahua to share their journalistic products during the first quarter of 2022 on Instagram. A mixed methodology was designed, and the content analysis technique was applied with an observation sheet to study 162 publications under three categories subdivided into the following aspects: geographical scope, journalistic genre, language, format, type of humor, interaction tools, topics and audience reactions. The study confirms the use of memes as a tool that enhances attention and viralization of informative content, as it is part of the new narratives that have boost the exercise of cyber journalism. However, there is evidence of the predominance of practices that do not promote higher levels of interaction, greater diversity of journalistic genres, or a wider view in the selection of topics and participation of virtual communities. In general, an omission was determined in the use of memes on political topics and promotion of cultural and health issues. Also, a directly proportional relationship was identified between the predominance of informative topics, still image formats and low audience participation.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".