When Pandemic Stories Become Personal Stories: Community Journalism and the Coverage of Health Inequalities
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Social media’s influence on journalistic norms and practices is a prominent theme in journalism studies. For small news organizations, there is not always a clear line between their public image and the online identities of their journalists. Focusing on such ambiguity, this article examines the integration of social media use and journalistic practice at The Local, an independent online news magazine based in Toronto, Canada, as well as its potential implications for community journalism. A qualitative thematic analysis of 300 tweets about the COVID-19 pandemic in Toronto, posted by the magazine’s official account and its two star journalists, revealed a unique journalistic approach that prioritized hyper-local, data-informed, and affective storytelling over the traditional norm of journalists as detached observers and information providers. This finding sheds light on how journalism practices at The Local and other comparable digital news startups may contribute to the revival of community journalism.
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.
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 | 0.024 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it