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.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".