Twittering the News: The Emergence of Ambient Journalism
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
This paper examines new para-journalism forms such as micro-blogging as “awareness systems ” that provide journalists with more complex ways of understanding and reporting on the subtleties of public communication. Traditional journalism defines fact as information and quotes from official sources, which have been identified as forming the vast majority of news and information content. This model of news is in flux, however, as new social media technologies such as Twitter facilitate the instant, online dissemination of short fragments of information from a variety of official and unofficial sources. This paper draws from literature on new communications technologies in computer science to suggest that these broad, asynchronous, lightweight and always-on systems are enabling citizens to maintain a mental model of news and events around them, giving rise to awareness systems that paper describes as ambient journalism. The emergence of ambient journalism brought about by the use of these new digital delivery systems and evolving communications protocols raises significant research questions for journalism scholars and professionals. This research offers an initial exploration of the impact of awareness systems on journalism norms and practices. It suggests that one of the future directions for journalism may be to develop approaches and systems that help the public negotiate and regulate the flow of awareness information, facilitating the collection and transmission of news.
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 | 0.003 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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