Newspapers, Images and Income Support Policy
Bibliographic record
Abstract
To what extent do different newspapers have different kinds of images associated with articles on the same topic? We investigate this research question by considering one of the most important Income Support Policies implemented in Italy in recent times (‘Reddito di cittadinanza’ - RdC) which generated a strong debate in public opinion. Focussing on the national wide media, we downloaded images associated with articles about RdC and by means of Image Captioning algorithms, we generate the description of them. Results show that different newspapers have images containing different objects. Some topics emerging from images published by newspapers are very exclusive and the sentiment associated with the text extracted from the images has a wide heterogeneity. Furthermore, right-hand newspapers show a lower sentiment compared with left-hand newspapers. Overall, the results confirm that the ideological stance associated with different media outlets is reflected also in the images associated with articles and that the integration of Image Captioning algorithms and Natural Language Processes is very promising in this research area.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.007 |
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; both teacher heads agree on what is shown here.
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".