The ‘Known Unknown’: Changes in the Media over the Last Quarter Century (2000–2025)
Bibliographic record
Abstract
Social media, generative AI, fake news and deepfake, influencer communication. Five concepts that have shaped the media landscape and market over the last 25 years. But how do they interact to form a system for describing changes in the media platforms, channels and content? Based on a review of the relevant literature (desktop research), the paper captures and describes four dominant paradigms of change that have emerged over the past quarter century, which have profoundly and irreversibly transformed our everyday practices, habits and attitudes in and through the media. These trends are: 1. changing media messages: the rise of autonomy and virtuality; 2. changing communities and audiences: the rise of personal agency; 3. changing information: the rise of the false; 4. changing representations: the rise of the artificial. The list of four we propose can, of course, be extended and narrowed. However, it is assumed that, by summarising them, we can see more clearly the phenomena and trends that characterise our present and draw conclusions for the future.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".