Bibliographic record
Abstract
This paper investigates the role of headlines in the framing of news articles in print media, analyzing how they act as pivotal tools in shaping public interpretation and perception. Drawing on the theoretical foundation laid by scholars such as Entman, Baden, and Parenti, this research highlights the influence of headline language, tone, and accompanying visuals in constructing a narrative. The study focuses on two case examples from Indian national dailies: The Times of India and The Telegraph. Through qualitative analysis, it reveals how headlines not only serve as attention-grabbing hooks but also reflect editorial intentions and sociopolitical leanings. The headline "Donald Trump’s Death" by TOI exemplifies linguistic play used for brand impact, while "It took 79 days for pain and shame to pierce into 56-inch skin" by The Telegraph illustrates emotional and political framing through satire and cultural reference. This paper argues that headlines, though often underestimated, operate as self-contained frames capable of influencing news reception, reader engagement, and even shaping political discourse. The findings prompt a reevaluation of the journalistic function of headlines, positioning them as critical elements of media framing theory.
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 | 0.009 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".