Superstructure Analysis in News Stories-A Contrastive Study of Superstructure in VOA, BBC, and NPR News
Bibliographic record
Abstract
In news discourse, it is necessary to conclude the general feature of news discourse. It is supposed that there is a standard structure or news schemata to organize the news report. As we all know that the beginning of the news is headline or lead, in the following, there are main event, comment, previous event, aftereffect, situation and so on. The author of this paper chose 60 pieces of news from VOA, BBC, and NPR. The author used news schemata to analyze each piece of news. The purpose of this paper is to find out whether the structure of news fits the schemata or not and further describe the writing features of each news station. Moreover, different news stations have different characteristics in writing styles. BBC news emphasizes the role of comment while reporting the event, but it pays little attention to introduce the situation or background information. VOA news has no distinct feature in comment and main event; however, it spends more words on introducing background information. There is one point the author wants to remind: though verbal reaction does not as important as other elements, BBC and NPR indeed contain this item; VOA news has no verbal reaction at all. VOA reporters do not prefer to quote the exact opinion from news actors. NPR prefers to pay more attention to main event, and pay little attention to aftereffect. From concluding news schemata, it is not only helpful to know reporting features of the stations, but also helpful to study news discourse in a more clear, systematic and interesting way.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".