Do the leads tell the whole story? An analysis of story leads of the Walkerton, Ontario<i>E. coli</i>contamination of drinking water supplies
Bibliographic record
Abstract
Despite the availability of a variety of information sources, the news media remain one of the dominant sources of representations of advances in science, technology, government policies and other issues important to society. Some researchers have argued that story leads are important for conveying the content of an entire story and are highly memorable. We seek to address two questions in this article: how reliable is the story lead as a proxy for full text; and to what extent does the emphasis of one type of framing in the story lead influence the details of what people may remember of a particular event? From an analysis of a large media dataset including Canadian national newspaper and televised broadcasts, we compare the content of story leads to the full story text. We also draw on comments from news reporters reflecting on the importance of the lead and story structure principles that they follow in constructing a news story. We compare the results from the media dataset to recollections from participants from general public focus groups. We argue that in this particular case study, the news frames that reporters emphasised (health – illnesses and deaths as a result of the contamination) do not correspond with what average Ontarians from 10 focus groups remembered. We also conclude that while media story leads are an important device used to structure news stories, they do not serve as a good proxy measure for what is actually covered in the full text.
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 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 | 0.006 | 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 | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".