Analysis of the communication and composition of the digital press in Argentina, Peru, Mexico and Colombia in 2022
Bibliographic record
Abstract
This research was carried out with the aim of identifying the level of communication and structuring of the graphic patterns of publications on social networks Facebook, Instagram and Twitter. For this, four cases were chosen that are among the most influential newspapers in Latin America in the first quarter of 2022. These newspapers with greater influence are El Comercio Perú, El Universal, El Tiempo and La Nación considered so by the acceptance of their readers in print editions as in online. To collect the information, the inductive method was used by which topics such as typography, grids, interlettering, compositional elements, among others, were classified. And the method of analysis that serves to evaluate the behavior of the compositional elements individually of each publication. The focus of the research is mixed, since qualitative data were acquired: compositional and quantitative properties: interactions with more scope in the quarter of 2022. Along with this, the statistical data covered by eye tracking as an evaluation team in publications with greater scope or acceptance. In total there are 56 publications evaluated through the files and eleven with eye tracking. The data provided by the two evaluations contribute to the validation of the hypothesis in a theoretical and practical way. Concluding that the management of the structuring of graphic elements effectively affects acceptance in social media. The use of equipment related to neuromarketing is recommended. Which support the results obtainedthrough the application of theory in the designs.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| 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) | 0.003 | 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".