Bibliographic record
Abstract
Digital News Report: Australia 2016, the second annual survey of news consumption in Australia, builds on last year's debut to provide a clearer picture than ever before of how news is currently being consumed both within Australia and globally, with a particular focus on digital news consumption and pathways to accessing the news. The report is part of a global survey encompassing 25 other countries: Austria, Belgium, urban Brazil, Canada, the Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Japan, South Korea, the Netherlands, Norway, Poland, Portugal, Spain, Sweden, Switzerland, urban Turkey, the UK, and the USA. Key Findings: Terrestrial TV (53.9%) and social media (52.2%) were reported as the most popular source of news in the week prior to the survey. But when asked for one main source of news, 37.6% replied TV; 27.4% replied online news; 18.5% replied social network services / blogs. Radio news programs (39.6%), printed newspapers (35.4%) and websites of newspapers (32.5%) were also widely used as sources of news. Traditional media news consumption – TV, print and radio – is regarded as the main source (52.3%) and this is reflected to some extent in online news consumption, where the top source of news among online media was through websites or apps of newspapers (21.7%). The Digital News Report: Australia 2016 is a collaboration between the News & Media Research Centre at the University of Canberra and the Reuters Institute for the Study of Journalism at the University of Oxford.
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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.051 | 0.061 |
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".