Changing state of media in Australia, Portfolio of articles: Alexandra Wake 2015-2019
Bibliographic record
Abstract
BACKGROUND: Digital disruption of the media industry in Australia is having a devastating impact on newsrooms and news practices not only in Australia but internationally. There is a lack of qualified analysis of changes to the news environment as it is unfolding in Australia. Changes in news practices in other countries are also impacting on the local news environment. The pace of change is making it difficult for governments, regulators and newsmakers to get a sense of the impact of the disruption as it is unfolding. CONTRIBUTION: Over a four-year period, these articles used my specialist expertise as a journalism scholar to make visible and make sense of the changes in the news media. My inside knowledge of the local industry, gained from work integrated learning, enabled a unique insight into these unfolding changes. The portfolio contributes to conversations about the sustainability of journalism in Australia. SIGNIFICANCE: The eight articles in this portfolio were commissioned by editors at The Conversation and Crikey in the light of digital disruption. Four of the eight articles had more than 30,000 readers. The Conversation articles were republished by the ABC, Mumbrella, Harvard University’s Nieman Journalism Lab, Mamamia, Canada’s J Source, and Australian Policy Online. The views I outline in these pieces were subsequently sought by interviewers from the ABC.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".