Which Enemy to Dance with? A New Role of Software Piracy in Influencing Antipiracy Strategies
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper studies how software firms should determine their antipiracy efforts and product prices. There are two unique aspects of our model. First, antipiracy efforts have both a direct effect and a cross effect on software piracy. Second, we capture two types of competitions when piracy exists: one between a legitimate product and its pirated counterpart, and the other between two pirated products. We show that due to pirated products’ buffer effect not studied before, eliminating piracy does not necessarily mean higher profit for firms. This reveals an unexplored advantage of desktop software comparing with Software as a Service that can eliminate piracy. Direct and cross effects have different impacts on firms’ decisions and profits. Opposite to what one might expect, when a firm’s antipiracy effort becomes more effective in increasing the cost of pirating its own product but not its competitor’s product, the firm becomes worse off under certain conditions. By contrast, if the effort’s cross effect is higher, therefore increasing the cost of pirating its competitor’s product, a firm will always be better off. The managerial implication is that if a firm ignores the cross effect, it could under-invest in anti-piracy effort, causing its profit to suffer.
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.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.007 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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 it