Growth and Dynamics of Maturing New Media Companies
Bibliographic record
Abstract
CONTENTS Cinzia dal Zotto, What is the New Economy? 3-9. J. Bradford DeLong and A. Michael Froomkin, Background, Questions, and Speculations for Tomorrow’s Economy. 11- 38. Aaron Braaten, The Anticipated Effect of the SuperNet on Alberta’s Media Industry. 41-54. Tobias Fredberg and Susanne Olilla, Big Brother: Analyzing the Media System Around a Reality TV Show. 55-71. Benedetta Prario and Giuseppe Richeri, Integration Strategies of a Niche Communication Company: The Case of Gambero Rosso. 73-85. Xin Xun Wu and Ji Yin Chen, The Changing Structure of Media Organizations and its Meaning During the Transformation of the Social and Economic System in China. 87-99. Jacqueline Pennings, Hans van Kranenburg and John Hagedoorn, Past, Present, and Future of the European Telecommunications Industry. 103-123. Giuseppe Pagani, Benedetta Prario, Fabiana Visentin and Yvonne Zorzi, The Key of Success, the Cause of Failure: A Comparative Analysis of Two UK Digital Television Companies. 125-137. Marco Gambaro, Growth in a Convergent World: The Bundle of TV and Telephone Services on the Fiber Optic Network, 139-153. Daeho Kim, The Impact of Digital Convergence on Broadcasting Management in Korea: Telecommunications Firms’ Entry into the Broadcasting Industry. 155-166. Franz Lehner, Will Peer-to-Peer Technologies Create New Business? 169-184. Yingzi Xu, The Successful Model of Overseas Investment in Chinese New Media Companies. 185-194. Robert C. Burns and T.Y. Lau, Censorship, Government, and the Computer Game Industry. 195-210. Cinzia dal Zotto, Managing Growth in Young Firms: A Matter of Theory or a Question of Practice? 211-233.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".