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.
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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 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".