Bibliographic record
Abstract
News media are the general public's main source for health and medical information. Journalists who cover health topics are tasked not only with “translating” the complexities of science and medicine for their audiences, but with raising people's health literacy levels and providing citizens with the information they need to make informed decisions about personal health issues. Health journalists share a number of common norms and practices inherent to the journalistic profession more generally, including a commitment to truth, accuracy, fairness, balance, and independence, and a perceived role as “watchdogs” who serve citizens by holding those in power to account. Despite its important role, health journalism is in trouble. In line with larger trends affecting the news media industry, there are fewer specialized health journalists working in newsrooms today. Journalists have less time and fewer resources to do more work, and are increasingly competing for audiences online, where social media and bloggers have become popular sources for health information and mis/disinformation. Health journalism has also been the target of much scholarly critique, including charges of uncritical reporting, fixating on overhyped “breakthroughs,” not representing the realities of medical research, and fostering high levels of audience distrust in both journalism and the medical field. Despite these challenges and critiques, the COVID‐19 pandemic has caused many to recognize the value and importance of health journalism, which creates opportunities to use the lessons learned from covering a global health crisis to improve the field's overall quality and conditions.
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 | 0.017 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.009 | 0.010 |
| Scholarly communication | 0.026 | 0.011 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.062 | 0.022 |
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".