Copyright's impact on data mining in academic research
Bibliographic record
Abstract
With the proliferation of digital data, data mining (DM)—in the sense of the discovery of valuable structures in large sets of data—is expected to increase the productivity of many types of research. This paper discusses how copyright affects DM by academic researchers. In some territories, academic DM is lawful if researchers have lawful access to input works. In other territories such as the European Union, lawful DM additionally requires specific consent by rights holders. Based on bibliometric data and quasi‐experimental research designs, we show that where academic DM requires specific rights holder consent: (1) DM publications make up a significantly lower share of total research output, and (2) stronger rule of law is associated with less DM research. To our knowledge, this study is the first to empirically document an adverse effect of intellectual property (IP) on innovation under particular circumstances. There is strong evidence that copyright exceptions or limitations promote the adoption of DM research.
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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 | 0.076 | 0.507 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.017 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".