Bibliographic record
Abstract
Consolidation refers to the expansion of media firms through mergers and acquisitions. Formally, it is distinct from the concentration of media markets, although the terms are often used interchangeably. To some observers, consolidation responds to the growth of new television networks and cable and satellite channels – e.g., MTV, HBO, ESPN, CNN, Fox News, Canal 1, A&E, Al Jazeera – and the splintering of audiences into niche markets (→ Television Networks; Cable Television; Satellite Television; Audience Segmentation; Arab Satellite TV News). Even the grip of the → Hollywood majors – Columbia, → Disney, Paramount, Twentieth Century Fox, Universal, and Warner Bros – over US and international film audiences appears to be slipping, with their share of the US market tumbling from 85 percent in 1994 to 66 percent in 2004. Throw into this mix the → Internet's endless websites, downloading services, social networking sites and innumerable blogs, and the idea of media concentration seems anachronistic. As Benjamin Compaine (2001) states, “the democracy of the marketplace may be flawed but it is … getting better, not worse.”
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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.025 | 0.026 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.041 | 0.007 |
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".