Bibliographic record
Abstract
In today's rapidly evolving media landscape, the role of news media in shaping public perception and opinion has become an increasingly important area of study. One of the most significant theoretical frameworks used to understand how the media influences its audience is media framing theory. Media framing refers to the way media outlets present and structure information to shape the public’s interpretation of events, issues, or individuals. According to this perspective, the media not only reports news but actively constructs reality by emphasizing certain aspects of an issue while downplaying or omitting others, thereby guiding how audiences perceive and evaluate the world around them. The concept of framing was first introduced by Erving Goffman (1974), who argued that individuals in everyday life use "frames" to interpret and make sense of experiences. In a similar way, media organizations apply specific frames to construct stories, highlighting certain elements that align with ideological, political, or social perspectives. These frames influence the way in which an event is understood, its importance is gauged, and how different social, political, and cultural narratives are created. A common example of framing can be seen in news coverage of political events, where the same event may be framed in different ways depending on whether the outlet adopts a liberal or conservative approach, or whether the coverage is focused on human rights, economic impact, or security concerns. Over the years, media framing research has explored how frames are created, the factors that influence framing decisions, and the impact these frames have on audiences. Scholars have identified various types of frames used by media outlets, including conflict frames, human interest frames, economic frames, and responsibility frames, among others. Each type of frame serves a particular purpose, whether it’s to inform, persuade, or shape the opinions of the audience. This study seeks to examine the role of media frames in news coverage, specifically focusing on how framing influences the public’s interpretation of news, the way issues are categorized, and how media narratives affect individuals' attitudes and behaviors. Given the power that media holds in shaping public discourse, understanding the dynamics of framing is essential for both media producers and consumers. By critically analyzing how media frames are constructed and their subsequent effects, this research aims to shed light on the intricate relationship between media content, audience perception, and the broader social implications of news representation.
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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 | 0.015 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.006 | 0.031 |
| Scholarly communication | 0.011 | 0.019 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".