Chapter 5: Economic and Econometric Analysis on Key Issues Raised in the 2011 and 2014 Surveys
Bibliographic record
Abstract
The High Technology Sector of the Licensing Executives Society (U.S.A. and Canada) conducted the second Deal Term and Royalty Survey in 2014. The 2014 Survey received 94 samples of licensing transactions occurred from 2011 to the first half of 2014. To explore the market dynamics, this report analyzes the combined data of the 2011 and 2014 Survey, which includes a total of 322 samples, the largest size of survey-based samples ever studied in licensing industry. This chapter reports the results from economic and econometric analysis of the major issues raised in the Surveys. The chapter starts with economic analysis of payment structuring and deal features. Econometric analysis is then performed on flat running royalty rates (expressed as the percentages of sales) and lump sum payments, respectively. The 2014 Survey added several questions about NPE’s functions and roles in IP transaction market. The analysis of these responses, as well as other NPE-related information gathered from the 2011 and 2014 Survey, sheds further light on many of the controversies over the economics and business models of NPEs.
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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 | 0.014 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 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".