Framing Income Inequality: How the Spanish Media Reported on Disparities during the First Year of the Pandemic
Bibliographic record
Abstract
This paper addresses the problem of how Spanish digital media reported income inequality during the first year of the COVID-19 pandemic. In this way, the goal was to study the framing of definition, contextual aspects, and depth. For this article, a tool was designed to analyse the content of the items. An analysis of news published by six digital media in Spain from March 2020 to February 2021 was conducted using content analysis. Within a sample of 2727 media stories in which there was a connection between the coronavirus and inequality, a stratified sample was used (n = 958) according to the content production by quarter and by media. The results of this study show that income inequality was the most common type of inequality reported in the media, and they cantered more on the micro level. Also, it appeared to be linked to the social gap and showed poverty as the main consequence. The frame was focused on social issues, international and national contexts, and expert sources. Finally, different levels of depth can be observed in the news items analysed, depending on the frame.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".